ISASPublikationen.bib

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@article{JAIF23_Frisch,
  author  = {Daniel Frisch and Uwe D. Hanebeck},
  title   = {{The Generalized Fibonacci Grid as Low-Discrepancy Point Set for Optimal Deterministic Gaussian Sampling}},
  pages   = {16--34},
  volume  = {18},
  number  = {1},
  year    = {2023},
  month   = jun, 
  journal = {Journal of Advances in Information Fusion},
  issn    = {1557-6418},
  url     = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/Pages%20from%20JAIF-v18-i1-2-16-34_0.pdf},
}

@inproceedings{IFAC23_Reith-Braun,
  author    = {Marcel Reith-Braun and Albert Bauer and Maximilian Staab and Florian Pfaff and Georg Maier and Robin Gruna and Thomas Längle and Jürgen Beyerer and Harald Kruggel-Emden and Uwe D. Hanebeck},
  title     = {{GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs}},
  booktitle = {Proceedings of the 22nd IFAC World Congress (IFAC 2023)},
  year      = {2023},
  address   = {Yokohama, Japan},
  month     = jul,
  PDF       = {IFAC23_Reith-Braun.pdf},
}

@inproceedings{IFAC23_Fennel,
  author    = {Michael Fennel and Lukas Driller and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Observability-based Placement of Inertial Sensors on Robotic Manipulators for Kinematic State Estimation}},
  booktitle = {Proceedings of the 22nd IFAC World Congress (IFAC 2023)},
  year      = {2023},
  address   = {Yokohama, Japan},
  month     = jul,
  PDF       = {IFAC23_Fennel.pdf},
}

@inproceedings{FUSION23_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {Progressive Bayesian Particle Flows Based on Optimal Transport Map Sequences},
  booktitle = {Proceedings of the 26th International Conference on Information Fusion (Fusion 2023)},
  address   = {Charleston, USA},
  year      = {2023},
  month     = jun,
  PDF       = {FUSION23_Hanebeck.pdf},
}

@inproceedings{FUSION23_Fennel,
  author    = {Michael Fennel and Serge Garbay and Antonio Zea and Uwe D. Hanebeck},
  title     = {Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments},
  booktitle = {Proceedings of the 26th International Conference on Information Fusion (Fusion 2023)},
  address   = {Charleston, USA},
  year      = {2023},
  month     = jun,
  PDF       = {Fusion23_Fennel.pdf},
}

@inproceedings{FUSION23_Ernst,
  author    = {Eugen Ernst and Florian Pfaff and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Multitarget-Multidetection Tracking Using the Kernel SME Filter}},
  booktitle = {Proceedings of the 26th International Conference on Information Fusion (Fusion 2023)},
  year      = {2023},
  address   = {Charleston, USA},
  month     = jun,
  PDF       = {FUSION23_Ernst.pdf},
}

@inproceedings{FUSION23_Reith-Braun,
  author    = {Marcel Reith-Braun and Jakob Thumm and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Approximate First-Passage Time Distributions for Gaussian Motion and Transportation Models}},
  booktitle = {Proceedings of the 26th International Conference on Information Fusion (Fusion 2023)},
  year      = {2023},
  address   = {Charleston, USA},
  month     = jun,
  PDF       = {Fusion23_Reith-Braun.pdf},
}

@inproceedings{FUSION23_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {Deterministic Sampling of Arbitrary Densities Using Equal Sphere Packing of Volume under the Density (PoVuD)},
  booktitle = {Proceedings of the 26th International Conference on Information Fusion (Fusion 2023)},
  address   = {Charleston, USA},
  year      = {2023},
  month     = jun,
  PDF       = {Fusion23_Frisch.pdf},
}

@inproceedings{ACC23_Ernst,
  author    = {Eugen Ernst and Florian Pfaff and Uwe D. Hanebeck and Marcus Baum},
  title     = {{The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking}},
  booktitle = {Proceedings of the 2023 American Control Conference (ACC 2023)},
  year      = {2023},
  address   = {San Diego, CA, USA},
  month     = may,
  PDF       = {ACC23_Ernst.pdf},
}

@inproceedings{ACC23_Vieth,
  author    = {Jonathan Vieth and Marcel Reith-Braun and Albert Bauer and Florian Pfaff and Georg Maier and Robin Gruna and Thomas Längle and Harald Kruggel-Emden and Uwe D. Hanebeck},
  title     = {{Improving Accuracy of Optical Sorters Using Closed-Loop Control of Material Recirculations}},
  booktitle = {Proceedings of the 2023 American Control Conference (ACC 2023)},
  year      = {2023},
  address   = {San Diego, CA, USA},
  month     = may,
  PDF       = {ACC23_Vieth.pdf},
}

@article{TAC23_Ristic,
  author   = {Marko Ristic and Benjamin Noack and Uwe D. Hanebeck},
  journal  = {IEEE Transactions on Automatic Control},
  title    = {Distributed Range-Only Localisation that Preserves Sensor and Navigator Privacies},
  year     = {2023},
  month    = mar,
  url      = {https://doi.org/10.1109/TAC.2023.3263740},
}

@article{TM23_Maier,
  title    = {Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting},
  author   = {Georg Maier and Marcel Reith-Braun and Albert Bauer and Robin Gruna and Florian Pfaff and Harald Kruggel-Emden and Thomas Längle and Uwe D. Hanebeck and Jürgen Beyerer},
  journal  = {tm -- Technisches Messen, De Gruyter},
  year     = {2023},
  doi      = {10.1515/teme-2023-0033},
  month    = may,
  url      = {https://doi.org/10.1515/teme-2023-0033}
}

@article{PowTec22_Bauer,
  title    = {{Numerical Modelling of an Optical Belt Sorter Using a DEM--CFD Approach Coupled with Particle Tracking Algorithm and Comparison with Experiments}},
  author   = {Albert Bauer and Georg Maier and Marcel Reith-Braun, Harald Kruggel-Emden and Florian Pfaff and Robin Gruna and Uwe D. Hanebeck and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  year     = 2022,
  month    = sep,
  journal  = {Powder Technology},
  URL      = {https://doi.org/10.1016/j.powtec.2018.09.003},
}


@article{JAIF22_Zea,
  author  = {Antonio Zea and Uwe D. Hanebeck},
  title   = {{Modeling Spatial Uncertainty for the iPad Pro Depth Sensor}},
  year    = {2022},
  journal = {Journal of Advances in Information Fusion},
  url = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/Pages%20from%20JAIF-v17-i2_1.pdf},
}

@article{Access22_Siebler,
  author={Benjamin Siebler and Stephan Sand and Uwe D. Hanebeck},
  journal={IEEE Access}, 
  title={Bayesian Cramér--Rao Lower Bound for Magnetic Field-Based Localization}, 
  year={2022},
  volume={10},
  pages={123080-123093},
  doi={10.1109/ACCESS.2022.3223693},
  PDF = {Access22_Siebler.pdf},
}
  
@inproceedings{FB22_Maier,
author = {Georg Maier and Marcel Reith-Braun and Albert Bauer and Robin Gruna and Florian Pfaff and Harald Kruggel-Emden and Thomas Längle and Uwe D. Hanebeck and Jürgen Beyerer},
title = {Machine Learning-Based Multiobject Tracking for Sensor-Based Sorting},
booktitle = {Proceedings of the Image Processing Forum 2022},
  year      = {2022},
  address   = {Karlsruhe, Germany},
  month     = nov,
  PDF = {FB22_Maier.pdf},
}

@article{CIT22_Bauer,
author = {Albert Bauer and Georg Maier and Marcel Reith-Braun and Harald Kruggel-Emden and Florian Pfaff and Robin Gruna and Uwe D. Hanebeck and Thomas Längle},
title = {Benchmarking a DEM--CFD Model of an Optical Belt Sorter by Experimental Comparison},
  journal = {Chemie Ingenieur Technik},
  year    = {2022},
  url     = {https://doi.org/10.1002/cite.202200124},
  PDF = {CIT22_Bauer.pdf},
}

@article{AT22_Fennel,
  author  = {Michael Fennel and Antonio Zea and Uwe D. Hanebeck},
  journal = {at -- Automatisierungstechnik},
  title   = {{Intuitive and Immersive Teleoperation of Robot Manipulators for Remote Decontamination}},
  year    = {2022},
  month   = oct,
  url     = {https://doi.org/10.1515/auto-2022-0057},
}

@inproceedings{MFI22_Baur,
  author    = {Tim Baur and Johannes Reuter and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Harmonic Functions for Three-Dimensional Shape Estimation in Cylindrical Coordinates}},
  booktitle = {Proceedings of the 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2022)},
  year      = {2022},
  address   = {Cranfield, United Kingdom},
  month     = sep,
  PDF = {MFI22_Baur.pdf},
}

@inproceedings{MFI22_Frisch,
  author    = {Daniel Frisch and Kailai Li and Uwe D. Hanebeck},
  title     = {{Optimal Sensor Placement for Multilateration Using Alternating Greedy Removal and Placement}},
  booktitle = {Proceedings of the 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2022)},
  year      = {2022},
  address   = {Cranfield, United Kingdom},
  month     = sep,
  PDF = {MFI22_Frisch.pdf},
}

@inproceedings{MFI22_Fennel,
  author    = {Michael Fennel and Lukas Driller and Antonio Zea und Uwe D. Hanebeck},
  title     = {{Calibration-free IMU-based Kinematic State Estimation for Robotic Manipulators}},
  booktitle = {Proceedings of the 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2022)},
  year      = {2022},
  address   = {Cranfield, United Kingdom},
  month     = sep,
  PDF = {MFI22_Fennel.pdf},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/MFI2022_Best_Paper_Award.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{Fusion22_Frisch-Rejection,
address = {Linköping, Sweden},
author = {Daniel Frisch and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Rejection Sampling from Arbitrary Multivariate Distributions Using Generalized Fibonacci Lattices}},
year = {2022},
PDF = {Fusion22_Frisch-Rejection.pdf},
}

@inproceedings{Fusion22_Frisch-PCD,
address = {Linköping, Sweden},
author = {Daniel Frisch and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Deterministic Sampling on the Circle Using Projected Cumulative Distributions}},
year = {2022},
PDF = {Fusion22_Frisch-PCD.pdf},
}

@inproceedings{Fusion22_Pfaff,
address = {Linköping, Sweden},
author = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{The State Space Subdivision Filter for SE(3)}},
year = {2022},
PDF = {Fusion22_Pfaff.pdf},
}

@inproceedings{Fusion22_Baur,
ddress = {Linköping, Sweden},
author = {Tim Baur and Johannes Reuter and Antonio Zea and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Extent Estimation of Sailing Boats Applying Elliptic Cones to 3D LiDAR Data}},
year = {2022},
PDF = {Fusion22_Baur.pdf},
}
@inproceedings{Fusion22_XianqingLi,
address = {Linköping, Sweden},
author = {Xianqing Li and Zhansheng Duan and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Recursive Joint Cramér-Rao Lower Bound for Nonlinear Parametric Systems with Colored Noises}},
year = {2022},
PDF = {Fusion22_XianqingLi.pdf},
}

@inproceedings{Fusion22_Li,
address = {Linköping, Sweden},
author = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Circular Discrete Reapproximation}},
year = {2022},
PDF = {Fusion22_Li.pdf},
annote    = {<b>Winner Best Paper Award: First Runner Up</b> <a href="https://isas.iar.kit.edu/pdf/award/FUSION2022_BEST_PAPER_AWARD_FIRST_RUNNER_UP.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{Fusion22_Prossel,
address = {Linköping, Sweden},
author = {Dominik Prossel and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on Information Fusion (Fusion 2022)},
month = jul,
title = {{Dirac Mixture Reduction Using Wasserstein Distances on Projected Cumulative Distributions}},
year = {2022},
PDF = {Fusion22_Prossel.pdf},
}

@inproceedings{Fusion22_Zea,
address = {Linköping, Sweden},
author = {Antonio Zea and Michael Fennel and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Robot Joint Tracking With Mobile Depth Cameras for Augmented Reality Applications}},
year = {2022},
PDF = {Fusion22_Zea.pdf},
}

@inproceedings{Fusion22_Noack,
address = {Linköping, Sweden},
author = {Benjamin Noack and Clemens Öhl and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Event-Based Kalman Filtering Exploiting Correlated Trigger Information}},
year = {2022},
PDF = {Fusion22_Noack.pdf},
}

@inproceedings{Fusion22_Zhang,
address = {Linköping, Sweden},
author = {Donglin Zhang and Zhansheng Duan and Uwe D. Hanebeck},
booktitle = {Proceedings of the 25th International Conference on
Information Fusion (Fusion 2022)},
month = jul,
title = {{Asynchronous Multi-Radar Tracking Fusion with Converted Measurements}},
year = {2022},
PDF = {Fusion22_Zhang.pdf},
}

@Article{Haptics21_Fennel,
	author={Michael Fennel and Antonio Zea and Uwe D. Hanebeck},
	journal={IEEE Transactions on Haptics},
	title={Optimization-Driven Design of a Kinesthetic Haptic Interface with Human-Like Capabilities},
	year = {2021},
	PDF = {Haptics21_Fennel.pdf},
}

@Article{TII21_Thumm,
	author={Jakob Thumm and Marcel Reith-Braun and Florian Pfaff and Uwe D. Hanebeck and Merle Flitter and Georg Maier and Robin Gruna and Thomas Längle and Albert Bauer and Harald Kruggel-Emden},
	journal={IEEE Transactions on Industrial Informatics},
	title={Mixture of Experts of Neural Networks and Kalman Filters for Optical Belt Sorting},
	month=sep,
	year = {2021},
	PDF = {TII21_Thumm.pdf},
}

@inproceedings{Fusion21_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {{Deterministic Gaussian Sampling With Generalized Fibonacci Grids}},
  booktitle = {Proceedings of the 24th International Conference on Information Fusion (Fusion 2021)},
  year      = {2021},
  address   = {Sun City, South Africa},
  month     = nov,
  pdf       = {Fusion21_Frisch.pdf}
}

@inproceedings{Fusion21_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{Deep Likelihood Learning for 2-D Orientation Estimation Using a Fourier Filter}},
  booktitle = {Proceedings of the 24th International Conference on Information Fusion (Fusion 2021)},
  year      = {2021},
  address   = {Sun City, South Africa},
  month     = nov,
  pdf       = {Fusion21_Pfaff.pdf}
}

@inproceedings{Fusion21_Dunik,
  author    = {Jind\v{r}ich Dun\'{i}k and Ond\v{r}ej Straka and Uwe D. Hanebeck},
  title     = {{Cooperative Unscented Kalman Filter with Bank of Scaling Parameter Values}},
  booktitle = {Proceedings of the 24th International Conference on Information Fusion (Fusion 2021)},
  year      = {2021},
  address   = {Sun City, South Africa},
  month     = nov,
  pdf       = {Fusion21_Dunik.pdf}
}

@inproceedings{MFI21_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{Conditional Densities and Likelihoods for Hypertoroidal Densities Based on Trigonometric Polynomials}},
  booktitle = {Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021)},
  year      = {2021},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {MFI21_Pfaff.pdf}
}

@inproceedings{MFI21_Zhao,
  author    = {Haibin Zhao and Christopher Funk and Benjamin Noack and Uwe D. Hanebeck and Michael Beigl},
  title     = {{Kalman Filtered Compressive Sensing Using Pseudo-Measurements}},
  booktitle = {Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021)},
  year      = {2021},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {MFI21_Zhao.pdf}
}

@inproceedings{MFI21_Radtke,
  author    = {Susanne Radtke and Uwe D. Hanebeck},
  title     = {{Learning and Exploiting Partial Knowledge in Distributed Estimation}},
  booktitle = {Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021)},
  year      = {2021},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {MFI21_Radtke.pdf}
}

@inproceedings{MFI21_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {{Gaussian Mixture Estimation from Weighted Samples}},
  booktitle = {Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021)},
  year      = {2021},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {MFI21_Frisch.pdf}
}

@inproceedings{MFI21_Baur,
  author    = {Tim Baur and Johannes Reuter and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Shape Estimation and Tracking using Spherical Double Fourier Series for Three-Dimensional Range Sensors}},
  booktitle = {Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021)},
  year      = {2021},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {MFI21_Baur.pdf}
}

@article{Sensors21_Pfaff,
  author  = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title   = {{The State Space Subdivision Filter for Estimation on SE(2)}},
  journal = {Sensors},
  year    = {2021},
  month   = sep,
  doi     = {https://doi.org/10.3390/s21186314},
  url     = {https://www.mdpi.com/1424-8220/21/18/6314}
}

@article{JAIF21_Radtke,
  author  = {Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck},
  title   = {{Fully Decentralized Estimation Using Square-Root Decompositions}},
  pages   = {3--16},
  volume  = {16},
  number  = {1},
  url     = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/3-16.pdf},
  year    = {2021},
  month   = jun,
  journal = {Journal of Advances in Information Fusion}
}

@article{SoftwareImpacts21_Fennel,
  title   = {{RTCF: A Framework for Seamless and Modular Real-Time Control with ROS}},
  journal = {Software Impacts},
  year    = {2021},
  month   = aug,
  issn    = {2665-9638},
  doi     = {https://doi.org/10.1016/j.simpa.2021.100109},
  url     = {https://www.sciencedirect.com/science/article/pii/S2665963821000427},
  author  = {Michael Fennel and Stefan Geyer and Uwe D. Hanebeck},
  pdf     = {SoftwareImpacts21_Fennel.pdf}
}

@inproceedings{ECC21_Westermann,
  author    = {Johannes Westermann and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Adaptive Sampling for Global Meta Modeling Using a Gaussian Process Variance Measure}},
  booktitle = {Proceedings of the 2021 European Control Conference (ECC 2021)},
  year      = {2021},
  address   = {Virtual},
  month     = jun,
  pdf       = {ECC21_Westermann.pdf}
}

@article{LCSS21_Ristic-2,
  author  = {Marko Ristic and Benjamin Noack and Uwe D. Hanebeck},
  title   = {{Cryptographically Privileged State Estimation With Gaussian Keystreams}},
  journal = {IEEE Control Systems Letters},
  issn    = {2475-1456},
  year    = 2021,
  month   = may,
  doi     = {10.1109/LCSYS.2021.3084405},
  url     = {https://doi.org/10.1109/LCSYS.2021.3084405},
  pdf     = {LCSS21_Ristic-2.pdf}
}

@article{LCSS21_Fennel,
  author   = {Michael Fennel and Antonio Zea and Johannes Mangler and Arne Roennau and Uwe D. Hanebeck},
  title    = {{Haptic Rendering of Arbitrary Serial Manipulators for Robot Programming}},
  journal  = {IEEE Control Systems Letters},
  year     = 2021,
  month    = jun,
  pdf      = {LCSS21_Fennel.pdf},
  abstract = {The programming of manipulators is a common task in robotics, for which numerous solutions exist. In this work, a new programming method related to the common master-slave approach is introduced, in which the master is replaced by a digital twin created through haptic and visual rendering. To achieve this, we present an algorithm that enables the haptic rendering of any programmed robot with a serial manipulator on a general-purpose haptic interface. The results show that the proposed haptic rendering reproduces the kinematic properties of the programmed robot and directly provides the desired joint space trajectories. In addition to a stand-alone usage, we demonstrate that the proposed algorithm can be easily paired with existing visual technology for virtual and augmented reality to facilitate a highly immersive programming experience.}
}

@article{Sensors21_Funk,
  author  = {Christopher Funk and Benjamin Noack and Uwe D. Hanebeck},
  title   = {{Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion}},
  journal = {Sensors},
  year    = {2021},
  month   = apr,
  doi     = {https://doi.org/10.3390/s21093059},
  url     = {https://www.mdpi.com/1424-8220/21/9/3059},
  pdf     = {Sensors21_Funk.pdf}
}

@article{CAE21_Maier,
  author   = {Georg Maier and Anja Shevchyk and Merle Flitter and Robin Gruna and Thomas Längle and Uwe D. Hanebeck and Jürgen Beyerer},
  journal  = {Computers and Electronics in Agriculture},
  title    = {Motion-Based Visual Inspection of Optically Indiscernible Defects on the Example of Hazelnuts},
  year     = {2021},
  month    = jun,
  issn     = {0168-1699},
  pages    = {106147},
  volume   = {185},
  abstract = {Automatic quality control has long been an integral part of the processing of food and agricultural products. Visual inspection offers solutions for many issues in this context and can be employed in the form of sensor-based sorting to automatically remove foreign and low quality entities from a product stream. However, these methods are limited to defects that can be made visible by the employed sensor, which usually restricts the system to defects appearing on the surface. An alternative non-visual solution lies in impact-acoustic methods, which do not suffer from this constraint. However, these are strongly limited in terms of material throughput and consequently not suitable for large scale industrial application. In this paper, we present a novel approach that performs inspection based on optically acquired motion data. A high-speed camera captures image sequences of test objects during a transportation process on a chute with a specific structured surface. The trajectory data is then used to classify test objects based on their motion behavior. The approach is evaluated experimentally on the example of distinguishing defect-free hazelnuts from ones that suffer from insect damage. Results show that by merely utilizing the motion data, a recognition rate of up to 80\% for undamaged hazelnuts can be achieved. A major advantage of our approach is that it can be integrated in sensor-based sorting systems and is suitable for high throughput applications.},
  doi      = {https://doi.org/10.1016/j.compag.2021.106147},
  keywords = {Object trajectory, Motion classification, Sensor-based Sorting, Impact-acoustic},
  url      = {https://www.sciencedirect.com/science/article/pii/S0168169921001654},
  pdf      = {CAE21_Maier.pdf}
}

@article{IEEESensors21_Siebler,
  author    = {Benjamin Siebler and Stephan Sand and Uwe D. Hanebeck},
  year      = {2021},
  month     = feb,
  title     = {Localization with Magnetic Field Distortions and Simultaneous Magnetometer Calibration},
  volume    = {21},
  number    = {3},
  pages     = {3388-3397},
  journal   = {IEEE Sensors Journal},
  doi       = {10.1109/JSEN.2020.3024073},
  publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  issn      = {1530-437X, 1558-1748},
  language  = {english},
  pdf       = {IEEESensors21_Siebler.pdf}
}

@article{RAL21_Li,
  author  = {Kailai Li and Meng Li and Uwe D. Hanebeck},
  title   = {{Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping}},
  journal = {IEEE Robotics and Automation Letters},
  year    = {2021},
  month   = jul,
  url     = {https://ieeexplore.ieee.org/document/9392274},
  pdf     = {RAL21_Li.pdf},
  vol     = 6,
  number  = 3
}

@article{Sensors21_Li,
  author  = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title   = {{Progressive von Mises--Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation}},
  journal = {Sensors},
  year    = {2021},
  month   = apr,
  doi     = {10.3390/s21092991},
  url     = {https://www.mdpi.com/1424-8220/21/9/2991},
  pdf     = {Sensors21_Li.pdf}
}

@article{RAL21_Fennel,
  author   = {Michael Fennel and Antonio Zea and Uwe D. Hanebeck},
  title    = {{Haptic-Guided Path Generation for Remote Car-Like Vehicles}},
  journal  = {IEEE Robotics and Automation Letters},
  year     = {2021},
  volume   = {6},
  number   = {2},
  pages    = {4088-4095},
  abstract = {Despite significant advances in robot autonomy, manual intervention by a human operator is necessary in many situations. This usually requires qualified staff and some robot-specific input device even for the comparatively simple case of platform locomotion. For this reason, we propose a novel path generation method applicable to car-like vehicles. With this method, the operator “draws” a desired 2D path by walking in a large-scale haptic interface while a guiding force is exerted, which ensures that the generated path can later be accurately followed by a path tracking controller running offline on a remote robot. We present a local optimization-based path planner, a higher-level path generation algorithm utilizing the aforementioned planner, and a force feedback law. Experiments show improved feasibility of the generated paths without affecting the operator's ability to make decisions independently.},
  keywords = {Haptic interfaces;Robots;Kinematics;Turning;Force;Legged locomotion;Trajectory;Haptics and haptic interfaces;human performance augmentation;motion and path planning;telerobotics and teleoperation},
  doi      = {10.1109/LRA.2021.3067846},
  issn     = {2377-3766},
  month    = apr,
  pdf      = {RAL21_Fennel.pdf}
}


@article{SIMP21_Zea,
  title    = {iviz: A ROS visualization app for mobile devices},
  journal  = {Software Impacts},
  volume   = {8},
  pages    = {100057},
  year     = {2021},
  month    = may,
  issn     = {2665-9638},
  doi      = {https://doi.org/10.1016/j.simpa.2021.100057},
  url      = {https://www.sciencedirect.com/science/article/pii/S2665963821000051},
  author   = {Antonio Zea and Uwe D. Hanebeck},
  keywords = {Robotics, Data visualization, Augmented reality}
}

@inproceedings{IROS20_Zea,
  address   = {Las Vegas, USA},
  author    = {Christoph Pohl and Kevin Hitzler and Raphael Grimm and Antonio Zea and Uwe D. Hanebeck and Tamim Asfour},
  booktitle = {Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)},
  month     = oct,
  title     = {Affordance-Based Grasping and Manipulation in Real World Applications},
  year      = {2020},
  pdf       = {IROS20_Zea.pdf}
}

@inproceedings{MFI20_Li,
  author    = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Nonlinear von Mises--Fisher Filtering Based on Isotropic Deterministic Sampling}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = sep,
  pdf       = {MFI20_Li.pdf}
}

@inproceedings{MFI20_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {{Efficient Deterministic Conditional Sampling of Multivariate Gaussian Densities}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = sep,
  pdf       = {MFI20_Frisch.pdf}
}

@inproceedings{MFI20_Funk,
  author    = {Christopher Funk and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Conservative Quantization of Fast Covariance Intersection}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = sep,
  pdf       = {MFI20_Funk.pdf}
}

@inproceedings{MFI20_Pollithy,
  author    = {Daniel Pollithy and Marcel Reith-Braun and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = sep,
  pdf       = {MFI20_Pollithy.pdf}
}

@inproceedings{MFI20_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{Estimating Correlated Angles Using the Hypertoroidal Grid Filter}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = sep,
  pdf       = {MFI20_Pfaff.pdf}
}

@inproceedings{IFAC20_Rosenthal,
  author    = {Florian Rosenthal and Uwe D. Hanebeck},
  title     = {{Stability Analysis of Polytopic Markov Jump Linear Systems with Applications to Sequence-Based Control over Networks}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Rosenthal.pdf}
}

@inproceedings{PLANS20_Siebler,
  author    = {Benjamin Siebler and Oliver Heirich and Stephan Sand and Uwe D. Hanebeck},
  booktitle = {2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)},
  title     = {Joint Train Localization and Track Identification based on Earth Magnetic Field Distortions},
  year      = {2020},
  volume    = {},
  number    = {},
  pages     = {941-948},
  abstract  = {In this paper a train localization method is proposed that uses local variations of the earth magnetic field to determine the topological position of a train in a track network. The approach requires a magnetometer triad, an accelerometer, and a map of the magnetic field along the railway tracks. The estimated topological position comprises the along-track position that defines the position of the train within a certain track and the track ID that specifies the track the train is driving on. The along-track position is estimated by a a recursive Bayesian filter and the track ID is found from a hypothesis test. In particular the use of multiple particle filter, each estimating the position on different track hypothesis, is proposed. Whenever the estimated train position crosses a switch, a particle filter for each possible track is created. With the position estimates of the different filters, the likelihood for each track hypothesis is calculated from the measured magnetic field and the expected magnetic field in the map. A comparison of the likelihoods is subsequently used to decide which track is the most likely. After a decision for a track is made, the unnecessary filters are deleted. The feasibility of the proposed localization method is evaluated with measurement data recorded on a regional train. In the evaluation, the localization method was running in real time and overall an RMSE below five meter could be achieved and all tracks were correctly identified.},
  keywords  = {accelerometers;Bayes methods;distortion;magnetometers;particle filtering (numerical methods);position measurement;rail traffic control;railways;recursive filters;target tracking;recursive Bayesian filter;track ID;multiple particle filter;track hypothesis;position estimates;measured magnetic field;expected magnetic field;regional train;joint train localization;earth magnetic field distortions;train localization method;track network;railway tracks;estimated topological position;along-track position;track identification;magnetometer triad;accelerometer;hypothesis test;particle filter;measurement data},
  doi       = {10.1109/PLANS46316.2020.9110149},
  issn      = {2153-3598},
  month     = apr,
  pdf       = {PLANS20_Siebler.pdf}
}


@inproceedings{IFAC20_Frisch,
  author    = {Daniel Frisch and Kailai Li and Uwe D. Hanebeck},
  title     = {{Optimal Reduction of Dirac Mixture Densities on the 2-Sphere}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Frisch.pdf}
}

@inproceedings{IFAC20_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{The Spherical Grid Filter for Nonlinear Estimation on the Unit Sphere}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Pfaff.pdf}
}

@inproceedings{IFAC20_Li-PoseGraph,
  author    = {Kailai Li and Johannes Cox and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Li-PoseGraph.pdf}
}

@inproceedings{IFAC20_Li-UPF,
  author    = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Hyperspherical Unscented Particle Filter for Nonlinear Orientation Estimation}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Li-UPF.pdf}
}

@inproceedings{IFAC20_Noack,
  author    = {Benjamin Noack and Christopher Funk and Susanne Radtke and Uwe D. Hanebeck},
  title     = {{State Estimation with Event-Based Inputs Using Stochastic Triggers}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Noack.pdf}
}

@inproceedings{IFAC20_Radtke,
  author    = {Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Reconstruction of Cross-Correlations between Heterogeneous Trackers Using Deterministic Samples}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Radtke.pdf}
}

@inproceedings{IFAC20_Mayer,
  author    = {Jana Mayer and Ajit Basarur and Mariana Petrova and Fabian Sordon and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Position and Speed Estimation of PMSMs Using Gaussian Processes}},
  booktitle = {Proceedings of the 1st Virtual IFAC World Congress (IFAC-V 2020)},
  year      = {2020},
  month     = jul,
  pdf       = {IFAC20_Mayer.pdf}
}

@article{LCSS21_Ristic,
  author   = {Marko Ristic and Benjamin Noack and Uwe D. Hanebeck},
  title    = {{Secure Fast Covariance Intersection Using Partially Homomorphic and Order Revealing Encryption Schemes}},
  journal  = {IEEE Control Systems Letters},
  issn     = {2475-1456},
  volume   = {5},
  number   = {1},
  pages    = {217--222},
  month    = jan,
  year     = 2021,
  abstract = {Fast covariance intersection is a widespread technique for state estimate fusion in sensor networks when cross-correlations are not known and fast computations are desired. The common requirement of sending estimates from one party to another during fusion forfeits local privacy. Current secure fusion algorithms rely on encryption schemes that do not provide sufficient flexibility. As a result, excess communication between estimate producers is required, which is often undesirable. We propose a novel method of homomorphically computing the fast covariance intersection algorithm on estimates encrypted with a combination of encryption schemes. Using order revealing encryption, we show how an approximate solution to the fast covariance intersection weights can be computed and combined with partially homomorphic encryptions of estimates, to calculate an encryption of the fused result. The described approach allows secure fusion of any number of private estimates, making third-party cloud processing a viable option when working with sensitive state estimates or when performing estimation over untrusted networks.},
  doi      = {10.1109/LCSYS.2020.3000649},
  url      = {https://doi.org/10.1109/LCSYS.2020.3000649},
  pdf      = {LCSS21_Ristic.pdf}
}

@article{LCSS21_Li,
  author  = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title   = {{Unscented Dual Quaternion Particle Filter for SE(3) Estimation}},
  journal = {IEEE Control Systems Letters},
  issn    = {2475-1456},
  year    = {2021},
  volume  = {5},
  number  = {2},
  month   = apr,
  pages   = {647--652},
  doi     = {10.1109/LCSYS.2020.3005066},
  url     = {https://doi.org/10.1109/LCSYS.2020.3005066},
  pdf     = {LCSS21_Li.pdf}
}

@inproceedings{Fusion20_Basarur,
  author    = {Ajit Basarur and Jana Mayer and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Position and Speed Estimation for BLDC Motors Using Fourier-Series Regression}},
  booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = jul,
  pdf       = {FUSION20_Basarur.pdf}
}

@inproceedings{Fusion20_Radtke,
  author    = {Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Fully Decentralized Estimation Using Square-Root Decompositions}},
  booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = jul,
  pdf       = {FUSION20_Radtke.pdf},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/FUSION2020_BEST_PAPER_AWARD.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{Fusion20_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {{Progressive Bayesian Filtering with Coupled Gaussian and Dirac Mixtures}},
  booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = jul,
  pdf       = {FUSION20_Frisch.pdf}
}

@inproceedings{Fusion20_Li,
  author    = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Dual Quaternion Sample Reduction for SE(2) Estimation}},
  booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = jul,
  pdf       = {FUSION20_Li.pdf}
}

@inproceedings{Fusion20_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{A Hyperhemispherical Grid Filter for Orientation Estimation}},
  booktitle = {Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = jul,
  pdf       = {FUSION20_Pfaff.pdf}
}

@inproceedings{CISS20_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Deterministic Sampling of Multivariate Densities based on Projected Cumulative Distributions}},
  year      = {2020},
  booktitle = {Proceedings of the 54th Annual Conference on Information Sciences and Systems (CISS 2020)},
  address   = {Princeton, New Jersey, USA},
  month     = mar,
  pdf       = {CISS20_Hanebeck.pdf}
}

@inproceedings{ICRA20_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {{Association-Free Multilateration Based on Times of Arrival}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA 2020)},
  address   = {Virtual},
  year      = {2020},
  month     = may,
  pdf       = {ICRA20_Frisch.pdf}
}

@inproceedings{ICRA20_Li,
  author    = {Hannes Möls and Kailai Li and Uwe D. Hanebeck},
  title     = {{Highly Parallelizable Plane Extraction for Organized Point Clouds Using Spherical Convex Hulls}},
  booktitle = {Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA 2020)},
  address   = {Virtual},
  year      = {2020},
  month     = may,
  pdf       = {ICRA20_Li.pdf}
}

@inproceedings{ECC20_Li,
  author    = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Grid-Based Quaternion Filter for SO(3) Estimation}},
  booktitle = {Proceedings of the 2020 European Control Conference (ECC 2020)},
  year      = {2020},
  address   = {Virtual},
  month     = may,
  pdf       = {ECC20_Li.pdf}
}

@proceedings{AT20_Pfaff-Editorial,
  editor  = {Florian Pfaff and Uwe D. Hanebeck},
  journal = {at -- Automatisierungstechnik},
  month   = apr,
  title   = {Guest Editorial of the Special Issue on Sensor-Based Sorting},
  year    = {2020},
  url     = {https://www.degruyter.com/view/journals/auto/68/4/article-p229.xml},
  doi     = {10.1515/auto-2020-0023}
}

@article{AT20_Pfaff,
  author  = {Florian Pfaff and Christoph Pieper and Georg Maier and Benjamin Noack and Robin Gruna and Harald Kruggel-Emden and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and Thomas Längle and Jürgen Beyerer},
  journal = {at -- Automatisierungstechnik},
  title   = {{Predictive Tracking with Improved Motion Models for Optical Belt Sorting}},
  year    = {2020},
  month   = apr,
  url     = {https://www.degruyter.com/view/journals/auto/68/4/article-p239.xml},
  doi     = {10.1515/auto-2019-0134}
}

@article{AT20_Maier,
  author  = {Georg Maier and Florian Pfaff and Andrea Bittner and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Uwe D. Hanebeck and Thomas Längle and Jürgen Beyerer},
  journal = {at -- Automatisierungstechnik},
  title   = {{Characterizing Material Flow in Sensor-Based Sorting Systems Using an Instrumented Particle}},
  year    = {2020},
  month   = apr,
  doi     = {10.1515/auto-2019-0128},
  url     = {https://www.degruyter.com/view/journals/auto/68/4/article-p256.xml}
}

@article{EURASIP20_Frisch,
  author  = {Daniel Frisch and Uwe D. Hanebeck},
  title   = {{Commentary to: TDOA versus ATDOA for Wide Area Multilateration System}},
  journal = {EURASIP Journal on Wireless Communications and Networking},
  year    = {2020},
  volume  = {2020},
  number  = {1},
  pages   = {43},
  month   = feb,
  issn    = {1687-1499},
  url     = {https://doi.org/10.1186/s13638-020-1656-1}
}

@article{TIE20_Maier,
  author  = {Georg Maier and Florian Pfaff and Christoph Pieper and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Thomas Längle and Uwe D. Hanebeck and Jürgen Beyerer},
  journal = {Transactions on Industrial Electronics},
  title   = {{Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking}},
  year    = {2020},
  month   = feb,
  url     = {https://ieeexplore.ieee.org/document/8984697},
  pdf     = {TIE20_Maier.pdf},
  doi     = {10.1109/TIE.2020.2970643}
}

@inproceedings{CDC19_Rosenthal,
  author    = {Florian Rosenthal and Uwe D. Hanebeck},
  title     = {{Sequence-Based Stochastic Receding Horizon Control Using IMM Filtering and Value Function Approximation}},
  booktitle = {Proceedings of the 58th IEEE Conference on Decision and Control (CDC 2019)},
  year      = {2019},
  address   = {Nice, France},
  month     = dec,
  pdf       = {CDC19_Rosenthal.pdf}
}

@inproceedings{ICCAIS19_Zhang,
  author    = {Qingpeng Zhang and Zhansheng Duan and Uwe D. Hanebeck},
  title     = {{Recursive LMMSE Centralized Fusion with Compressed Multi-Radar Measurements}},
  booktitle = {Proceedings of the 2019 International Conference on Control, Automation and Information Sciences (ICCAIS 2019)},
  year      = {2019},
  address   = {Chengdu, China},
  month     = oct,
  pdf       = {ICCAIS2019_Zhang.pdf}
}

@inproceedings{ACC19_Rosenthal,
  author    = {Florian Rosenthal and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Sequence-Based Receding Horizon Control Over Networks with Delays and Data Losses}},
  booktitle = {Proceedings of the 2019 American Control Conference (ACC 2019)},
  year      = {2019},
  address   = {Philadelphia, PA, USA},
  month     = jul,
  pdf       = {ACC19_Rosenthal.pdf}
}

@inproceedings{ACC19_Mayer,
  author    = {Jana Mayer and Maxim Dolgov and Tobias Stickling and Selim Özgen and Uwe D. Hanebeck},
  title     = {{Stochastic Optimal Control Using Gaussian Process Regression Over Probability Distributions}},
  booktitle = {Proceedings of the 2019 American Control Conference (ACC 2019)},
  year      = {2019},
  address   = {Philadelphia, PA, USA},
  month     = jul,
  pdf       = {ACC19_Mayer.pdf}
}


@article{LCSS19_Schmitt,
  author   = {Eva Julia Schmitt and Benjamin Noack and Wolfgang Krippner and Uwe D. Hanebeck},
  title    = {{Gaussianity-Preserving Event-Based State Estimation with an FIR-Based Stochastic Trigger}},
  journal  = {IEEE Control Systems Letters},
  issn     = {2475-1456},
  volume   = {3},
  number   = {3},
  pages    = {769--774},
  month    = jul,
  year     = 2019,
  abstract = {With modern communication technology, sensors, estimators, and controllers can be pushed apart to distribute 
              intelligence over wide distances. Instead of congesting channels by periodic data transmissions, smart sensors can decide 
              on their own whether data are worth transmitting. This paper studies event-based transmissions from sensor to estimator. 
              The sensor-side event trigger conveys usable information even if no transmission is triggered. In the absence of data, 
              such implicit information can still be exploited by the remote Kalman filter. For this purpose, an easy-to-implement 
              triggering mechanism is proposed based on a Finite Impulse Response prediction that is compared against a stochastic 
              decision variable. By the aid of the stochastic event trigger, the implicit information retains a Gaussian representation 
              and can easily be processed by the Kalman filter. The parameters for the stochastic trigger are retrieved from the 
              Finite Impulse Response filter, which contributes to reducing the communication rate significantly, as shown in simulations.},
  doi      = {10.1109/LCSYS.2019.2918024},
  url      = {https://doi.org/10.1109/LCSYS.2019.2918024},
  pdf      = {LCSS19_Schmitt.pdf}
}


@inproceedings{Fusion19_Zea,
  author    = {Antonio Zea and Uwe D. Hanebeck},
  title     = {{Refining Pose Estimation for Square Markers Using
               Shape Fitting}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Zea.pdf}
}

@inproceedings{Fusion19_Noack,
  author    = {Benjamin Noack and Umut Orguner and Uwe D. Hanebeck},
  title     = {{Nonlinear Decentralized Data Fusion with Generalized 
               Inverse Covariance Intersection}},
  booktitle = {Proceedings of the 22nd International Conference on 
               Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  abstract  = {Decentralized data fusion is a challenging task even for 
               linear estimation problems. Nonlinear estimation renders data fusion 
               even more difficult as dependencies among the nonlinear estimates 
               require complicated parameterizations. It is nearly impossible to 
               reconstruct or keep track of dependencies. Therefore, conservative 
               approaches have become a popular solution to nonlinear data fusion. As a 
               generalization of Covariance Intersection, exponential mixture densities 
               have been widely applied for nonlinear fusion. However, this approach 
               inherits the conservativeness of Covariance Intersection. For this 
               reason, the less conservative fusion rule Inverse Covariance 
               Intersection is studied in this paper and also generalized to nonlinear 
               data fusion. This generalization employs a conservative approximation of 
               the common information shared by the estimates to be fused. This bound 
               of the common information is subtracted from the fusion result. In doing 
               so, less conservative fusion results can be attained as an empirical 
               analysis demonstrates.},
  pdf       = {Fusion19_Noack.pdf}
}

@inproceedings{Fusion19_Frisch,
  author    = {Daniel Frisch and Uwe D. Hanebeck},
  title     = {{ROTA: Round Trip Times of Arrival for Localization with Unsynchronized Receivers}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Frisch.pdf}
}


@inproceedings{Fusion19_Shao,
  author    = {Teng Shao and Zhangsheng Duan and Uwe D. Hanebeck},
  title     = {{Multi-Rate Asynchronous Distributed Filtering Under Randomized Gossip Strategy}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Shao.pdf}
}

@inproceedings{Fusion19_Bultmann,
  author    = {Simon Bultmann and Kailai Li and Uwe D. Hanebeck},
  title     = {{Stereo Visual SLAM Based on Unscented Dual Quaternion Filtering}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Bultmann.pdf}
}

@inproceedings{Fusion19_Li,
  author    = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Hyperspherical Deterministic Sampling Based on Riemannian Geometry for Improved Nonlinear Bingham Filtering}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Li.pdf}
}

@inproceedings{Fusion19_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{Fourier Filters, Grid Filters, and the Fourier-Interpreted Grid Filter}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Pfaff.pdf}
}

@inproceedings{Fusion19_Kronauer,
  author    = {Tobias Kronauer and Florian Pfaff and Benjamin  Noack and Wei Tian and Georg Maier and Uwe D. Hanebeck},
  title     = {{Feature-Aided Multitarget Tracking for Optical Belt Sorters}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Kronauer.pdf}
}

@inproceedings{Fusion19_Radtke,
  author    = {Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Distributed Estimation using Square Root Decompositions of Dependent Information}},
  booktitle = {Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019)},
  year      = {2019},
  address   = {Ottawa, Canada},
  month     = jul,
  pdf       = {Fusion19_Radtke.pdf}
}

@inproceedings{ECC19_Radtke,
  author    = {Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Distributed Estimation with Partially Overlapping States based on Deterministic Sample-based Fusion}},
  booktitle = {Proceedings of the 2019 European Control Conference (ECC 2019)},
  year      = {2019},
  address   = {Naples, Italy},
  month     = jun,
  pdf       = {ECC19_Radtke.pdf}
}

@inproceedings{ECC19_Li,
  author    = {Kailai Li and Daniel Frisch and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Geometry-Driven Deterministic Sampling for Nonlinear Bingham Filtering}},
  booktitle = {Proceedings of the 2019 European Control Conference (ECC 2019)},
  year      = {2019},
  address   = {Naples, Italy},
  month     = jun,
  pdf       = {ECC19_Li.pdf}
}

@article{JSS19_Kurz,
  author  = {Gerhard Kurz and Igor Gilitschenski and Florian Pfaff and Lukas Drude and Uwe D. Hanebeck and Reinhold Haeb-Umbach and Roland Y. Siegwart},
  journal = {Journal of Statistical Software},
  title   = {{Directional Statistics and Filtering Using libDirectional}},
  year    = {2019},
  month   = may,
  url     = {https://dx.doi.org/10.18637/jss.v089.i04},
  doi     = {10.18637/jss.v089.i04}
}

@inproceedings{MFI19_Oezgen,
  author    = {Selim Özgen and Saskia Kohn and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{State Estimation with Model-Mismatch-Based Secrecy against Eavesdroppers}},
  booktitle = {Proceedings of the 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019)},
  year      = {2019},
  address   = {Taipei, Republic of China},
  month     = may,
  pdf       = {MFI19_Oezgen.pdf}
}

@inproceedings{ICPS19_Rosenthal,
  author    = {Florian Rosenthal and Uwe D. Hanebeck},
  title     = {{A Control Approach for Cooperative Sharing of Network Resources in Cyber-Physical Systems}},
  booktitle = {Proceedings of the 2019 IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2019)},
  year      = {2019},
  address   = {Taipei, Republic of China},
  month     = may,
  pdf       = {ICPS19_Rosenthal.pdf}
}

@inproceedings{MFI19_Radtke,
  author    = {Susanne Radtke and Kailai Li and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-only Measurements}},
  booktitle = {Proceedings of the 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019)},
  year      = {2019},
  address   = {Taipei, Republic of China},
  month     = may,
  pdf       = {MFI19_Radtke.pdf}
}

@inproceedings{MFI19_Li,
  author    = {Kailai Li and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Geometry-Driven Stochastic Modeling of SE(3) States Based on Dual Quaternion Representation}},
  booktitle = {Proceedings of the 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019)},
  year      = {2019},
  address   = {Taipei, Republic of China},
  month     = may,
  pdf       = {MFI19_Li.pdf}
}

@inproceedings{MFI19_Pfaff,
  author    = {Florian Pfaff and Kailai Li and Uwe D. Hanebeck},
  title     = {{Association Likelihoods for Directional Estimation}},
  booktitle = {Proceedings of the 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019)},
  year      = {2019},
  address   = {Taipei, Republic of China},
  month     = may,
  pdf       = {MFI19_Pfaff.pdf}
}

@inproceedings{CCNC19_Rosenthal,
  author    = {Florian Rosenthal and Markus Jung and Martina Zitterbart and Uwe D. Hanebeck},
  title     = {{CoCPN} -- Towards Flexible and Adaptive {Cyber-Physical} Systems Through Cooperation},
  booktitle = {Proceedings of the 2019 16th IEEE Annual Consumer Communications \& Networking Conference (CCNC 2019)},
  address   = {Las Vegas, USA},
  days      = 11,
  month     = jan,
  year      = 2019,
  keywords  = {Cyber-Physical Systems; Cooperation; Simulation; Event-Based Control},
  abstract  = {This work is concerned with our ongoing research
               project CoCPN: Cooperative Cyber Physical Networking that
               has the goal to allow cooperation between control applications
               and the communication system that constitute cyber-physical
               systems. We describe the envisioned architecture of CoCPN and
               outline how it improves the flexibility of cyber-physical systems
               by cooperatively sharing a common network infrastructure. We
               also present our simulation tool CoCPN-Sim that we developed
               as a method to thoroughly investigate the interaction between
               control applications and the communication system. By providing
               the results of selected simulations using the well-known inverted
               pendulum, we identify potential aspects that can be exploited
               for cooperation and hence serve as starting points towards more
               flexible and adaptive cyber-physical systems.},
  pdf       = {CCNC19_Rosenthal.pdf}
}

@article{PowTec18_Pieper,
  title    = {{Numerical Modelling of an Optical Belt Sorter Using a DEM--CFD Approach Coupled with Particle Tracking Algorithm and Comparison with Experiments}},
  author   = {Christoph Pieper and Florian Pfaff and Georg Maier and Harald Kruggel-Emden and Siegmar Wirtz and Benjamin Noack and Robin Gruna and Viktor Scherer and Uwe D. Hanebeck and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  year     = 2018,
  month    = dec,
  journal  = {Powder Technology},
  doi      = {10.1016/j.powtec.2018.09.003},
  url      = {https://doi.org/10.1016/j.powtec.2018.09.003},
  abstract = {State-of-the-art optical sorting systems suffer from delays between the particle detection and separation stage, during which the material movement is not accounted for. Commonly line scan cameras, using simple assumptions to predict the future particle movement, are employed. In this study, a novel prediction approach is presented, where an area scan camera records the particle movement over multiple time steps and a tracking algorithm is used to reconstruct the corresponding paths to determine the time and position at which the material reaches the separation stage. In order to assess the benefit of such a model at different operating parameters, an automated optical belt sorter is numerically modelled and coupled with the tracking procedure. The Discrete Element Method (DEM) is used to describe the particle–particle as well as particle–wall interactions, while the air nozzles required for deflecting undesired material fractions are described with Computational Fluid Dynamics (CFD). The accuracy of the employed numerical approach is ensured by comparing the separation results of a predefined sorting task with experimental investigations. The quality of the aforementioned prediction models is compared when utilizing different belt lengths, nozzle activation durations, particle types, sampling frequencies and detection windows. Results show that the numerical model of the optical belt sorter is able to accurately describe the sorting system and is suitable for detailed investigation of various operational parameters. The proposed tracking prediction model was found to be superior to the common line scan camera method in all investigated scenarios. Its advantage is especially profound when difficult sorting conditions, e.g. short conveyor belt lengths or uncooperative moving bulk solids, apply.}
}

@inproceedings{SDF18_Li,
  title     = {{Wavefront Orientation Estimation Based on Progressive Bingham Filtering}},
  author    = {Kailai Li and Daniel Frisch and Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck},
  year      = 2018,
  month     = oct,
  booktitle = {Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2018)},
  abstract  = {In this paper, we propose the Progressive Bingham
               Filter (PBF), a novel stochastic filtering algorithm for nonlinear
               spatial orientation estimation. As an extension of the orientation
               filter previously proposed only for the identity measurement
               model based on the Bingham distribution, our method is able to
               handle arbitrary measurement models. Instead of the sampling-
               approximation scheme used in the prediction step, a closed-
               form solution is possible when the system equation is based on
               the Hamilton product. Besides stochastic approaches, we also
               introduce the Spherical Averaging Method (SAM), which is an
               application of the Riemannian averaging technique. The two
               approaches are then applied to a specific problem where the
               wavefront orientation is estimated based on Time Differences of
               Arrival (TDOA) and evaluated in simulations. The results show
               theoretical competitiveness of the PBF.},
  pdf       = {SDF18_Li.pdf}
}

@inproceedings{Fusion18_Oezgen,
  author    = {Selim Özgen and Marco F. Huber and Florian Rosenthal and Jana Mayer and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Retrodiction of Data Association Probabilities via Convex Optimization}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  month     = jul,
  pdf       = {Fusion18_Oezgen.pdf}
}

@inproceedings{Fusion18_Radtke,
  author    = {Susanne Radtke and Benjamin Noack and Uwe D. Hanebeck and Ond\v{r}ej Straka},
  title     = {{Reconstruction of Cross-Correlations with Constant Number of Deterministic Samples}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  abstract  = {Optimal fusion of estimates that are computed in a distributed fashion is a challenging task. In general, the sensor nodes cannot keep track of the cross-correlations required to fuse estimates optimally. In this paper, a novel technique is presented that provides the means to reconstruct the required correlation structure. For this purpose, each node computes a set of deterministic samples that provides all the information required to reassemble the cross-covariance matrix for each pair of estimates. As the number of samples is increasing over time, a method to reduce the size of the sample set is presented and studied. In doing so, communication expenses can be reduced significantly, but approximation errors are possibly introduced by neglecting past correlation terms. In order to keep approximation errors at a minimum, an appropriate set size can be determined and a trade-off between communication expenses and estimation quality can be found.},
  month     = jul,
  pdf       = {Fusion18_Radtke.pdf}
}

@inproceedings{Fusion18_Li-SE2,
  author    = {Kailai Li and Gerhard Kurz and Lukas Bernreiter and Uwe D. Hanebeck},
  title     = {{Nonlinear Progressive Filtering for SE(2) Estimation}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  month     = jul,
  pdf       = {Fusion18_Li-SE2.pdf}
}

@inproceedings{Fusion18_Li-SLAM,
  author    = {Kailai Li and Gerhard Kurz and Lukas Bernreiter and Uwe D. Hanebeck},
  title     = {{Simultaneous Localization and Mapping Using a Novel Dual Quaternion Particle Filter}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  month     = jul,
  pdf       = {Fusion18_Li-SLAM.pdf}
}

@inproceedings{Fusion18_Aristov,
  author    = {Mikhail Aristov and Benjamin Noack and Uwe D. Hanebeck and J{\"{o}}rn M{\"{u}}ller-Quade},
  title     = {{Encrypted Multisensor Information Filtering}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  abstract  = {With the advent of cheap sensor technology, multisensor data fusion algorithms have been becoming a key enabler for efficient in-network processing of sensor data. The information filter, in particular, has proven useful due to its simple additive structure of the measurement update equations.
               In order to exploit this structure for an efficient in-network processing, each node in the network is supposed to locally process and combine data from its neighboring nodes. The aspired in-network processing, at first glance, prohibits efficient privacy-preserving communication protocols, and encryption schemes that allow for algebraic manipulations are often computationally too expensive. Partially homomorphic encryption schemes constitute far more practical solutions but are restricted to a single algebraic operation on the corresponding ciphertexts. In this paper, an additive-homomorphic encryption scheme is used to derive a privacy-preserving implementation of the information filter where additive operations are sufficient to distribute the workload among the sensor nodes. However, the encryption scheme requires the floating-point data to be quantized, which impairs the estimation quality. The proposed filter and the implications of the necessary quantization are analyzed in a simulated multisensor tracking scenario.},
  month     = jul,
  pdf       = {Fusion18_Aristov.pdf}
}

@inproceedings{Fusion18_Sigges,
  author    = {Fabian Sigges and Christoph Rauterberg and Marcus Baum and Uwe D. Hanebeck},
  title     = {{An Ensemble Kalman Filter for Feature-Based SLAM with Unknown Associations}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  month     = jul,
  pdf       = {Fusion18_Sigges.pdf}
}

@inproceedings{Fusion18_Havlik,
  author    = {Jind\v{r}ich Havl\'{i}k and Ond\v{r}ej Straka and Uwe D. Hanebeck},
  title     = {{Stochastic Integration Filter: Theoretical and Implementation Aspects}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  month     = jul,
  pdf       = {Fusion18_Havlik.pdf}
}

@inproceedings{Fusion18_Wang,
  author    = {Qi Wang and Zhansheng Duan and Xiao-Rong Li and Uwe D. Hanebeck},
  title     = {{Convex Combination for Source Localization Using Received Signal Strength Measurements}},
  booktitle = {Proceedings of the 21st International Conference on Information Fusion (Fusion 2018)},
  year      = {2018},
  address   = {Cambridge, United Kingdom},
  month     = jul,
  pdf       = {Fusion18_Wang.pdf}
}

@article{Sensors2018_Dormann,
  author   = {Katharina Dormann and Benjamin Noack and Uwe D. Hanebeck},
  title    = {{Optimally Distributed Kalman Filtering with Data-Driven Communication}},
  journal  = {Sensors},
  year     = {2018},
  volume   = {18},
  number   = {4},
  month    = apr,
  issn     = {1424-8220},
  abstract = {For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node.},
  doi      = {10.3390/s18041034},
  url      = {https://www.mdpi.com/1424-8220/18/4/1034},
  pdf      = {Sensors18_Dormann.pdf}
}

@incollection{LNEE18_Rosenthal,
  author    = {Florian Rosenthal and Benjamin Noack and Uwe D. Hanebeck},
  editor    = {Sukhan Lee and Hanseok Ko and Songhwai Oh},
  title     = {{State Estimation in Networked Control Systems with Delayed and Lossy Acknowledgments}},
  booktitle = {Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System},
  year      = {2018},
  publisher = {Springer International Publishing},
  address   = {Cham},
  pages     = {22--38},
  abstract  = {In this article, we are concerned with state estimation in Networked Control Systems where both control inputs and measurements are transmitted over networks which are lossy and introduce random transmission delays. We focus on the case where acknowledgment packets transmitted by the actuator upon reception of applicable control inputs are also subject to delays and losses, as opposed to the common notion of TCP-like communication where successful transmissions are acknowledged instantaneously and without losses. As a consequence, the state estimator in the considered setup has only partial and belated knowledge concerning the actually applied control inputs which results in additional uncertainty. We derive an estimator by extending an existing approach for the special case of UDP-like communication which maintains estimates of the applied control inputs that are incorporated into the estimation of the plant state. The presented estimator is compared to the original approach in terms of Monte Carlo simulations where its increased robustness towards imperfect knowledge of the underlying networks is indicated.},
  isbn      = {978-3-319-90509-9},
  doi       = {https://doi.org/10.1007/978-3-319-90509-9_2},
  url       = {https://link.springer.com/chapter/10.1007/978-3-319-90509-9_2}
}

@article{IETSP2018_Dunik,
  author   = {Jind\v{r}ich Dun\'{i}k and Ond\v{r}ej Straka and Benjamin Noack and Jannik Steinbring and Uwe D. Hanebeck},
  title    = {{On Directional Splitting of Gaussian Density in Nonlinear Random Variable Transformation}},
  journal  = {IET Signal Processing},
  year     = {2018},
  month    = jul,
  issn     = {1751-9683},
  pages    = {1073-1081},
  volume   = {12},
  number   = {9},
  abstract = {Transformation of a random variable is a common need in a design of many algorithms in signal processing, automatic
              control, and fault detection. Typically, the design is tied to an assumption on a probability density function of the random variable,
              often in the form of the Gaussian distribution. The assumption may be, however, difficult to be met in algorithms involving nonlinear
              transformation of the random variable. This paper focuses on techniques capable to ensure validity of the Gaussian assumption of
              the nonlinearly transformed Gaussian variable by approximating the to-be-transformed random variable distribution by a Gaussian
              mixture distribution. The stress is laid on an analysis and selection of design parameters of the approximate Gaussian mixture
              distribution to minimise the error imposed by the nonlinear transformation such as the location and number of the Gaussian mixture
              terms. A special attention is devoted to the definition of the novel Gaussian mixture splitting directions based on the measures of
              non-Gaussianity. The proposed splitting directions are analysed and illustrated in numerical simulations.},
  doi      = {10.1049/iet-spr.2017.0286},
  url      = {https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0286}
}

@inproceedings{ACC18_Dolgov,
  author    = {Maxim Dolgov and Gerhard Kurz and Daniela Grimm and Florian Rosenthal and Uwe D. Hanebeck},
  title     = {{Stochastic Optimal Control Using Local Sample-Based Value Function Approximation}},
  booktitle = {Proceedings of the 2018 American Control Conference (ACC 2018)},
  year      = {2018},
  address   = {Milwaukee, Wisconsin, USA},
  month     = jun,
  pdf       = {ACC18_Dolgov.pdf}
}

@inproceedings{ACC18_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Improved Progressive Gaussian Filtering Using LRKF Priors}},
  booktitle = {Proceedings of the 2018 American Control Conference (ACC 2018)},
  year      = {2018},
  pdf       = {ACC18_Kurz-PGF.pdf},
  address   = {Milwaukee, Wisconsin, USA},
  month     = jun
}

@inproceedings{ACC18_Rosenthal,
  author    = {Florian Rosenthal and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Scheduling of Measurement Transmission in Networked Control Systems Subject to Communication Constraints}},
  booktitle = {Proceedings of the 2018 American Control Conference (ACC 2018)},
  year      = {2018},
  pdf       = {ACC18_Rosenthal.pdf},
  address   = {Milwaukee, Wisconsin, USA},
  month     = jun
}

@inproceedings{IoTDI18_Jung,
  author    = {Markus Jung and Florian Rosenthal and Martina Zitterbart},
  title     = {{Poster Abstract: CoCPN-Sim: An Integrated Simulation Environment for Cyber-Physical Systems}},
  booktitle = {Proceedings of the 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)},
  year      = {2018},
  address   = {Orlando, FL, USA},
  month     = apr,
  annote    = {<b>Best Poster Award Runner-Up</b>}
}

@proceedings{TII18_Hanebeck,
  editor  = {Uwe D. Hanebeck and Marcus Baum and Marco F. Huber},
  journal = {IEEE Transactions on Industrial Informatics},
  title   = {Guest Editorial Special Section on Multisensor Fusion and Integration for Intelligent Systems},
  year    = {2018},
  volume  = {14},
  month   = mar
}

@article{ITS18_Li,
  author   = {Ziqi Ye and Kailai Li and Michael Stapelbroek and Rene Savelsberg and Marco Günther and Stefan Pischinger},
  journal  = {IEEE Transactions on Intelligent Transportation Systems},
  title    = {Variable Step-Size Discrete Dynamic Programming for Vehicle Speed Trajectory Optimization},
  year     = {2018},
  volume   = {PP},
  number   = {99},
  pages    = {1-9},
  keywords = {Computational efficiency;Intelligent transportation systems;Real-time systems;Time-domain analysis;Trajectory optimization;Discrete dynamic programming;V2X;intelligent transportation systems;intelligent vehicles;predictive driving.},
  doi      = {10.1109/TITS.2018.2812921},
  issn     = {1524-9050},
  month    = {}
}

@inproceedings{SBSC18_Maier,
  author    = {Georg Maier and Florian Pfaff and Christoph Pieper and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Thomas L{\"{a}}ngle and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and J{\"{u}}rgen Beyerer},
  title     = {Application of Area-Scan Sensors in Sensor-Based Sorting},
  booktitle = {Proceedings of the Eighth Conference on Sensor-Based Sorting \& Control 2018 (SBSC 2018)},
  year      = {2018},
  address   = {Aachen, Germany},
  month     = mar,
  pdf       = {SBSC18_Maier.pdf},
  abstract  = {In the field of machine vision, sensor-based sorting is an important real-time application that enables the separation of a material feed into different classes. While state-of-the-art systems utilize scanning sensors such as line-scan cameras, advances in sensor technology have made application of area scanning sensors feasible. Provided a sufficiently high frame rate, objects can be observed at multiple points in time. By applying multiobject tracking, information about the objects contained in the material stream can be fused over time. Based on this information, our approach further allows predicting the position of each object for future points in time. While conventional systems typically apply a global, rather simple motion model, our approach includes an individual motion model for each object, which in turn allows estimating the point in time as well as the position when reaching the separation stage. In this contribution, we present results from our collaborative research project and summarize the present advances by discussing the potential of the application of area-scan sensors for sensor-based sorting. Among others, we introduce our simulation-driven approach and present results for physical separation efficiency for simulation-generated data, demonstrate the potential of using motion-based features for material classification and discuss real-time related challenges.}
}

@article{TII18_Kurz,
  author  = {Gerhard Kurz and Florian Pfaff and Uwe D. Hanebeck},
  journal = {IEEE Transactions on Industrial Informatics},
  title   = {Application of Discrete Recursive Bayesian Estimation on Intervals and the Unit Circle to Filtering on SE(2)},
  volume  = {14},
  number  = {3},
  month   = mar,
  pages   = {1197-1206},
  pdf     = {TII18_Kurz-DiscreteFiltering.pdf},
  url     = {https://doi.org/10.1109/TII.2017.2757011},
  doi     = {10.1109/TII.2017.2757011},
  year    = {2018}
}

@article{RTIP17_Maier,
  author   = {Georg Maier and Florian Pfaff and Matthias Wagner and Christoph Pieper and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Thomas L{\"{a}}ngle and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and J{\"{u}}rgen Beyerer},
  title    = {Real-Time Multitarget Tracking for Sensor-Based Sorting},
  journal  = {Journal of Real-Time Image Processing},
  year     = {2017},
  abstract = {Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes.},
  doi      = {10.1007/s11554-017-0735-y},
  month    = nov,
  url      = {https://doi.org/10.1007/s11554-017-0735-y}
}

@article{TAC17_Dolgov,
  author   = {Maxim Dolgov and Uwe D. Hanebeck},
  journal  = {IEEE Transactions on Automatic Control},
  title    = {Static Output-Feedback Control of Markov Jump Linear Systems Without Mode Observation},
  year     = {2017},
  month    = oct,
  volume   = {62},
  number   = {10},
  pdf      = {TAC17_Dolgov.pdf},
  abstract = {In this paper, we address infinite-horizon optimal control
              of Markov Jump Linear Systems (MJLS) via static output feedback. Because
              the jump parameter is assumed not to be observed, the optimal control
              law is nonlinear and intractable. Therefore, we assume the regulator to
              be linear. Under this assumption, we first present sufficient
              feasibility conditions for static output-feedback stabilization of MJLS
              with non-observed mode in the mean square sense in terms of linear
              matrix inequalities (LMIs). However, these conditions depend on the
              particular state-space representation, i.e., a coordinate transform can
              make the LMIs feasible, while the original LMIs are infeasible. To avoid
              the issues with the ambiguity of the state-space representation, we
              therefore present an iterative algorithm for the computation of the
              regulator gain. The algorithm is shown to converge if the MJLS is
              stabilizable via mode-independent static output feedback. However,
              convergence of the algorithm is not sufficient for stability of the
              closed loop, which requires an additional stability check after the
              regulator gains have been computed. A numerical example demonstrates the
              application of the presented results.},
  doi      = {10.1109/TAC.2017.2703924},
  issn     = {0018-9286},
  url      = {https://ieeexplore.ieee.org/document/7927447/}
}

@article{TM17_Maier,
  title    = {Motion-Based Material Characterization in Sensor-Based Sorting},
  author   = {Georg Maier and Florian Pfaff and Florian Becker and Christoph Pieper and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Thomas L{\"{a}}ngle and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and J{\"{u}}rgen Beyerer},
  journal  = {tm -- Technisches Messen, De Gruyter},
  year     = {2017},
  doi      = {10.1515/teme-2017-0063},
  abstract = {Sensor-based sorting provides state-of-the-art solutions for sorting cohesive, granular materials. Typically, involved sensors, illumination, implementation of data analysis and other components are designed and chosen according to the sorting task at hand. A common property of conventional systems is the utilization of scanning sensors. However, the usage of area-scan cameras has recently been proposed. When observing objects at multiple time points, the corresponding paths can be reconstructed by using multiobject tracking. This in turn allows to accurately estimate the point in time and position at which any object will reach the separation stage of the optical sorter and hence contributes to decreasing the error in physical separation. In this paper, it is proposed to further exploit motion information for the purpose of material characterization. By deriving suitable features from the motion information, we show that high classification performance is obtained for an exemplary classification task. The approach therefore contributes towards decreasing the detection error of sorting systems.},
  month    = oct,
  url      = {https://doi.org/10.1515/teme-2017-0063}
}

@inproceedings{PARTICLES17_Pieper,
  title     = {Numerical Modelling of the Separation of Complex Shaped Particles in an Optical Belt Sorter Using a {DEM}--{CFD} Approach and Comparison with Experiments},
  author    = {Christoph Pieper and Georg Maier and Florian Pfaff and Harald Kruggel-Emden and Robin Gruna and Benjamin Noack and Siegmar Wirtz and Viktor Scherer and Thomas L{\"{a}}ngle and Uwe D. Hanebeck and J{\"{u}}rgen Beyerer},
  booktitle = {Proceedings of the 5th International Conference on Particle-Based Methods (PARTICLES 2017)},
  address   = {Hannover, Germany},
  year      = {2017},
  abstract  = {In the growing field of bulk solids handling, automated optical sorting systems are
               of increasing importance. However, the initial sorter calibration is still very time consuming
               and the precise optical sorting of many materials still remains challenging. In order to
               investigate the impact of different operating parameters on the sorting quality, a numerical
               model of an existing modular optical belt sorter is presented in this study. The sorter and particle
               interaction is described with the Discrete Element Method (DEM) while the air nozzles required
               for deflecting undesired material fractions are modelled with Computation Fluid Dynamics
               (CFD). The correct representation of the resulting particle–fluid interaction is realized through
               a one–way coupling of the DEM with CFD. Complex shaped particle clusters are employed to
               model peppercorns also used in experimental investigations. To test the correct implementation
               of the utilized models, the particle mass flow within the sorter is compared between experiment
               and simulation. The particle separation results of the developed numerical model of the optical
               sorting system are compared with matching experimental investigations. The findings show
               that the numerical model is able to predict the sorting quality of the optical sorting system with
               reasonable accuracy.},
  pdf       = {Particles17_Pieper.pdf},
  month     = sep
}

@inproceedings{CDC17_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Linear Regression Kalman Filtering Based on Hyperspherical Deterministic Sampling}},
  booktitle = {Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017)},
  year      = {2017},
  address   = {Melbourne, Australia},
  pdf       = {CDC17_Kurz-HSKF.pdf},
  month     = dec
}

@inproceedings{MFI17_Dormann,
  author    = {Katharina Dormann and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Distributed Kalman Filtering With Reduced Transmission Rate}},
  booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
  year      = {2017},
  address   = {Daegu, Republic of Korea},
  month     = nov,
  pdf       = {MFI17_Dormann.pdf},
  abstract  = {The centralized Kalman filter can be implemented
               in such a way that the required calculations can be distributed
               over multiple nodes in a network, each of which processes
               only the locally acquired sensor data. The main downside of
               this implementation is that it requires each distributed sensor
               node to communicate with the fusion center in every time step
               so as to compute the optimal state estimate. In this paper,
               two distributed Kalman filtering algorithms are proposed to
               overcome these limitations. The first algorithm merely requires
               communication of each local sensor node with the fusion center
               in every other time step. The second algorithm even allows
               for a lower communicate rate. Both algorithms apply event-based 
               communication to compute consistent estimates and to
               reduce the estimation error for a fixed communication rate.
               Simulations demonstrate that both algorithms perform better in
               terms of the mean squared estimation error than the centralized
               Kalman filter.}
}

@inproceedings{MFI17_Pfaff,
  author    = {Florian Pfaff and Gerhard Kurz and Christoph Pieper and Georg Maier and Benjamin Noack and Harald Kruggel-Emden and Robin Gruna and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  title     = {{Improving Multitarget Tracking Using Orientation Estimates for Sorting Bulk Materials}},
  booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
  year      = {2017},
  address   = {Daegu, Republic of Korea},
  month     = nov,
  pdf       = {MFI17_Pfaff.pdf},
  abstract  = {Optical belt sorters can be used to sort a large
               variety of bulk materials. By the use of sophisticated algo-
               rithms, the performance of the complex machinery can be
               further improved. Recently, we have proposed an extension
               to industrial optical belt sorters that involves tracking the
               individual particles on the belt using an area scan camera. If the
               estimated behavior of the particles matches the true behavior,
               the reliability of the separation process can be improved. The
               approach relies on multitarget tracking using hard association
               decisions between the tracks and the measurements. In this
               paper, we propose to include the orientation in the assessment
               of the compatibility of a track and a measurement. This allows
               us to achieve more reliable associations, facilitating a higher
               accuracy of the tracking results.}
}

@inproceedings{MFI17_Pfaff-SphericalHarmonics,
  author    = {Florian Pfaff and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Filtering on the Unit Sphere Using Spherical Harmonics}},
  booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
  year      = {2017},
  address   = {Daegu, Republic of Korea},
  pdf       = {MFI17_Pfaff-SphericalHarmonics.pdf},
  abstract  = {For manifolds with topologies that strongly differ from the standard topology of R^n, using common filters created for linear domains can yield misleading results. While there is a lot of ongoing research on estimation on the unit circle, higher-dimensional problems particularly pose a challenge. One important generalization of the unit circle is the unit hypersphere. In this paper, we propose a recursive Bayesian estimator for the unit sphere S^2 based on spherical harmonics for arbitrary likelihood functions and rotationally symmetric system noises. In our evaluation, the proposed filter outperforms the particle filter in a target tracking scenario on the sphere.},
  month     = nov
}

@inproceedings{MFI17_Kurz,
  author    = {Gerhard Kurz and Florian Pfaff and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
  title     = {{Discretization of SO(3) Using Recursive Tesseract Subdivision}},
  year      = {2017},
  address   = {Daegu, Republic of Korea},
  month     = nov,
  abstract  = {The group of rotations in three dimensions SO(3) plays a crucial role in applications ranging from robotics and aeronautics to computer graphics. Rotations have three degrees of freedom, but representing rotations is a nontrivial matter and different methods, such as Euler angles, quaternions, rotation matrices, and Rodrigues vectors are commonly used. Unfortunately, none of these representations allows easy discretization of orientations on evenly spaced grids. We present a novel discretization method that is based on a quaternion representation in conjunction with a recursive subdivision scheme of the four-dimensional hypercube, also known as the tesseract.},
  pdf       = {MFI17_Kurz-Tesseract.pdf}
}

@inproceedings{MFI17_Rosenthal,
  author    = {Florian Rosenthal and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{State Estimation in Networked Control Systems With Delayed And Lossy Acknowledgments}},
  booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
  year      = {2017},
  pdf       = {MFI17_Rosenthal.pdf},
  address   = {Daegu, Republic of Korea},
  month     = nov,
  abstract  = {In this paper, we consider state estimation in
               Networked Control Systems where both control inputs and
               measurements are transmitted via networks which are lossy
               and introduce random transmission delays. In contrast to the
               common notion of TCP-like communication, where successful
               transmissions are acknowledged instantaneously and without
               losses, we focus on the case where the acknowledgment packets
               provided by the actuator upon reception of applicable control
               inputs are also subject to delays and losses. Consequently,
               the estimator has only partial and belated knowledge on the
               actually applied control inputs, which results in additional
               uncertainty. We derive an estimator for the considered setup by
               generalizing an existing approach for UDP-like communication
               which integrates estimates of the applied control inputs into the
               overall state estimation. The presented estimator is assessed in
               terms of Monte Carlo simulations.}
}

@inproceedings{MFI17_Oezgen,
  author    = {Selim {\"{O}}zgen and Florian Faion and Antonio Zea and Uwe D. Hanebeck},
  title     = {{A Non-Parametric Inference Technique for Shape Boundaries in Noisy Point Clouds}},
  booktitle = {Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)},
  year      = {2017},
  address   = {Daegu, Republic of Korea},
  month     = nov,
  abstract  = {This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape information is not known, the offered algorithm estimates points on the shape boundary by using the statistics of the subsets of point cloud data.
               
               The novel approach proposed in this paper is able to find corner points in a local geometry by only using sample mean and covariance matrices of the subsets of the point cloud. While the proposed approach can be used for any class of boundary functions that demonstrates symmetry; for this paper, the analysis and experiments are performed on a connected line segment.},
  pdf       = {MFI17_Oezgen.pdf}
}

@inproceedings{Fusion17_Kurz,
  author    = {Gerhard Kurz and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Nonlinear Toroidal Filtering Based on Bivariate Wrapped Normal Distributions}},
  booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
  year      = {2017},
  address   = {Xi'an, China},
  month     = jul,
  pdf       = {Fusion17_Kurz-TorusFilter.pdf}
}

@inproceedings{Fusion17_Li,
  author    = {Xianqing Li and Zhansheng Duan and Uwe D. Hanebeck},
  title     = {{Performance Ranking of Multiple Nonlinear Filters Using Ranking Vector and Voting Fusion}},
  booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
  year      = {2017},
  address   = {Xi'an, China},
  month     = jul
}

@inproceedings{Fusion17_Noack,
  author    = {Benjamin Noack and Joris Sijs and Uwe D. Hanebeck},
  title     = {{Inverse Covariance Intersection: New Insights and Properties}},
  booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
  year      = {2017},
  address   = {Xi'an, China},
  month     = jul,
  pdf       = {Fusion17_Noack.pdf},
  abstract  = {Decentralized data fusion is a challenging task.
               Either it is too difficult to maintain and track the information
               required to perform fusion optimally, or too much information
               is discarded to obtain informative fusion results. A well-known
               solution is Covariance Intersection, which may provide too
               conservative fusion results. A less conservative alternative is
               discussed in this paper, and generalizations are proposed in
               order to apply it to a wide class of fusion problems. The Inverse
               Covariance Intersection algorithm is about finding the maximum
               possible common information shared by the estimates to be
               fused. A bound on the possibly shared common information
               is derived and removed from the fusion result in order to
               guarantee consistency. It is shown that the conditions required
               for consistency can be significantly relaxed, and also other causes
               of correlations, such as common process noise, can be treated.}
}


@inproceedings{Fusion17_Pfaff-Set,
  title     = {{Optimal Distributed Combined Stochastic and Set-Membership State Estimation}},
  author    = {Florian Pfaff and Benjamin Noack and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
  year      = {2017},
  address   = {Xi'an, China},
  month     = jul,
  pdf       = {Fusion17_Pfaff-Set.pdf},
  abstract  = {For distributed estimation, algorithms have to be
               specifically crafted to minimize communication between the
               sensor nodes. As an adjusted version of the regular Kalman filter,
               the distributed Kalman filter (DKF) allows for deriving optimal
               results while not requiring regular communication. To achieve
               this, the DKF requires that each node has full knowledge about
               the system model and measurement models of all nodes. However,
               the DKF is not sufficient if the characteristics of the errors in the
               system and measurement models are not purely stochastic. In this
               paper, we present a distributed version of a combined stochastic
               and set-membership Kalman filter. The proposed filter optimizes
               the approximations of the set-membership uncertainties and can
               even yield better results than the regular centralized filter.}
}

@inproceedings{Fusion17_Pfaff-IDKF,
  title     = {{Information Form Distributed Kalman Filtering (IDKF) with Explicit Inputs}},
  author    = {Florian Pfaff and Benjamin Noack and Uwe D. Hanebeck and Felix Govaers and Wolfgang Koch},
  booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
  year      = {2017},
  address   = {Xi'an, China},
  month     = jul,
  pdf       = {Fusion17_Pfaff-IDKF.pdf},
  abstract  = {With the ubiquity of information distributed in
               networks, performing recursive Bayesian estimation using
               distributed calculations is becoming more and more important.
               There are a wide variety of algorithms catering to different
               applications and requiring different degrees of knowledge about
               the other nodes involved. One recently developed algorithm is
               the distributed Kalman filter (DKF), which assumes that all
               knowledge about the measurements, except the measurements
               themselves, are known to all nodes. If this condition is met,
               the DKF allows deriving the optimal estimate if all information
               is combined in one node at an arbitrary time step. In this
               paper, we present an information form of the distributed Kalman
               filter (IDKF) that allows the use of explicit system inputs at
               the individual nodes while still yielding the same results as a
               centralized Kalman filter.}
}

@inproceedings{Fusion17_Sigges,
  title     = {{A Likelihood-Free Particle Filter for Multi-Object Tracking}},
  author    = {Fabian Sigges and Marcus Baum and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 20th International Conference on Information Fusion (Fusion 2017)},
  year      = {2017},
  address   = {Xi'an, China},
  month     = jul,
  pdf       = {Fusion17_Sigges.pdf},
  abstract  = {We present a particle filter for multi-object tracking
               that is based on the ideas of the Approximate Bayesian
               Computation (ABC) paradigm. The main idea is to avoid the explicit
               computation of the likelihood function by means of simulation.
               For this purpose, a large amount of particles in the state space is
               simulated from the prior, transformed into measurement space,
               and then compared to the real measurement by using an appropriate
               distance function, i.e., the OSPA distance. By selecting the
               closest simulated measurements and their corresponding particles
               in state space, the posterior distribution is approximated. The
               algorithm is evaluated in a multi-object scenario with and without
               clutter and is compared to a global nearest neighbour Kalman
               filter.}
}

@inproceedings{IFAC17_Sijs,
  author    = {Joris Sijs and Benjamin Noack},
  title     = {{Event-based State Estimation in a Feedback Loop with Imperfect Communication Links}},
  booktitle = {Proceedings of the 20th IFAC World Congress (IFAC 2017)},
  year      = {2017},
  address   = {Toulouse, France},
  month     = jul,
  abstract  = {Event-based sampling of sensor signals has become a mature alternative to time-periodic sampling completed with solutions for event-based estimation and control. Among those solutions there is a class of estimators exploiting the fact that an event was not triggered. Not receiving a new measurement is then interpreted as a sensor signal that has not violated the event criteria, which means that the signal is still within the triggering set defining the event. Such implied measurement information is exploited by the estimator, though it is only valid when no event occurred. The approach is thus sensitive to package loss and latency, as the estimator might be incorrect in assuming that no event took place. A solution to anticipate for package loss on the estimation error is studied in this article, and it is further turned into a first solution when the estimator is part of a feedback-control loop.}
}

@article{AT17_Pfaff,
  title    = {Real-Time Motion Prediction Using the Chromatic Offset of Line Scan Cameras},
  author   = {Florian Pfaff and Georg Maier and Mikhail Aristov and Benjamin Noack and Robin Gruna and Uwe D. Hanebeck and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer and Christoph Pieper and Harald Kruggel-Emden and Siegmar Wirtz and Viktor Scherer},
  journal  = {at -- Automatisierungstechnik, De Gruyter},
  pdf      = {AT17_Pfaff.pdf},
  month    = jun,
  abstract = {State-of-the-art optical belt sorters commonly employ line scan cameras and use simple assumptions to predict each particle's movement, which is required for the separation process. Previously, we have equipped an experimental optical belt sorter with an area scan camera and were able to show that tracking the particles of the bulk material results in an improvement of the predictions and thus also the sorting process. In this paper, we use the slight gap between the sensor lines of an RGB line scan camera to derive information about the particles' movements in real-time. This approach allows improving the predictions in optical belt sorters without necessitating any hardware modifications.},
  doi      = {10.1515/auto-2017-0009},
  url      = {https://doi.org/10.1515/auto-2017-0009},
  year     = {2017}
}

@inproceedings{SPIE17_Hanebeck,
  title     = {{Symmetrizing Measurement Equations for Association-free Multi-target Tracking via Point Set Distances}},
  author    = {Uwe D. Hanebeck and Marcus Baum and Peter Willett},
  booktitle = {SPIE - Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI},
  year      = {2017},
  address   = {Anaheim, California, USA},
  month     = apr,
  abstract  = {We are tracking multiple targets based on noisy measurements. The targets are labeled, the measurements are unlabeled, and the association of measurements to targets is unknown. Our goal is association-free tracking, so the associations will never be determined as this is costly and impractical in many scenarios. By employing a permutation-invariant and differentiable point set distance measure, we derive a modified association-free multi-target measurement equation. It maintains the target identities but is invariant to permutations in the unlabeled measurements. Based on this measurement equation, we derive an efficient sample-based association-free multi-target Kalman filter. The proposed new approach is straightforward to implement and scalable.},
  pdf       = {SPIE17_Hanebeck.pdf}
}

@article{Automatica17_Noack,
  title    = {{Decentralized Data Fusion with Inverse Covariance Intersection}},
  author   = {Benjamin Noack and Joris Sijs and Marc Reinhardt and Uwe D. Hanebeck},
  journal  = {Automatica},
  year     = {2017},
  month    = may,
  pages    = {35--41},
  volume   = {79},
  abstract = {In distributed and decentralized state estimation systems, fusion methods are employed to systematically combine multiple estimates of the state into a single, more accurate estimate. An often encountered problem in the fusion process relates to unknown common information that is shared by the estimates to be fused and is responsible for correlations. If the correlation structure is unknown to the fusion method, conservative strategies are typically pursued. As such, the parameterization introduced by the ellipsoidal intersection method has been a novel approach to describe unknown correlations, though suitable values for these parameters with proven consistency have not been identified yet. In this article, an extension of ellipsoidal intersection is proposed that guarantees consistent fusion results in the presence of unknown common information. The bound used by the novel approach corresponds to computing an outer ellipsoidal bound on the intersection of inverse covariance ellipsoids. As a major advantage of this 	inverse covariance intersection method, fusion results prove to be more accurate than those provided by the well-known covariance intersection method.},
  doi      = {10.1016/j.automatica.2017.01.019},
  url      = {https://dx.doi.org/10.1016/j.automatica.2017.01.019},
  pdf      = {Automatica17_Noack.pdf}
}

@inproceedings{OCM17_Maier,
  title     = {{Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information}},
  author    = {Georg Maier and Florian Pfaff and Florian Becker and Christoph Pieper and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Thomas L{\"{a}}ngle and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and J{\"{u}}rgen Beyerer},
  booktitle = {Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017)},
  year      = {2017},
  address   = {Karlsruhe, Germany},
  month     = mar,
  abstract  = {Sensor-based sorting provides state-of-the-art solutions for sorting of cohesive, granular materials. Systems tailored to a task at hand, for instance by means of sensors and implementations of data analysis. Conventional systems utilize scanning sensors which do not allow for extraction of motion-related information of objects contained in a material feed. Recently, usage of area-scan cameras to overcome this disadvantage has been proposed. Multitarget tracking can then be used in order to accurately estimate the point in time and position at which any object will reach the separation stage. In this paper, utilizing motion information of objects which can be retrieved from multitarget tracking for the purpose of classification is proposed. Results show that corresponding features can significantly increase classification performance and eventually decrease the detection error of a sorting system.},
  pdf       = {OCM17_Maier.pdf},
  url       = {https://www.ksp.kit.edu/9783731506126},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/OCM17_Best_Paper_Award.pdf" target="_blank">Certificate (PDF)</a>}
}

@article{TAES17_Zea,
  author   = {Antonio Zea and Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title    = {Level-Set Random Hypersurface Models for Tracking Nonconvex Extended Objects},
  journal  = {IEEE Transactions on Aerospace and Electronic Systems},
  year     = {2017},
  url      = {https://ieeexplore.ieee.org/document/7855600},
  abstract = {This paper presents a novel approach to track a nonconvex shape approximation of an extended target based on noisy point measurements. For this purpose, a novel type of random hypersurface model (RHM) called Level-set RHM is introduced that models the interior of a shape with level-sets of an implicit function. Based on the Level-set RHM, a nonlinear measurement equation can be derived that allows to employ a standard Gaussian state estimator for tracking an extended object even in scenarios with moderate measurement noise. In this paper, shapes are described using polygons, and shape regularization is applied using ideas from active contour models.}
}

@article{TAES17_Kurz,
  author  = {Gerhard Kurz and Uwe D. Hanebeck},
  title   = {{Deterministic Sampling on the Torus for Bivariate Circular Estimation}},
  journal = {IEEE Transactions on Aerospace and Electronic Systems},
  volume  = {53},
  number  = {1},
  month   = feb,
  pages   = {530--534},
  doi     = {10.1109/TAES.2017.2650079},
  issn    = {0018-9251},
  pdf     = {TAES17_Kurz.pdf},
  year    = {2017}
}


@inproceedings{CDC16_Dolgov,
  author    = {Maxim Dolgov and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Finite-horizon Dynamic Compensation of Markov Jump Linear Systems without Mode Observation}},
  booktitle = {Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016)},
  year      = {2016},
  address   = {Las Vegas, Nevada, USA},
  pdf       = {CDC16_Dolgov.pdf},
  month     = dec
}


@article{JAIF16_Kurz,
  author  = {Gerhard Kurz and Igor Gilitschenski and Roland Y. Siegwart and Uwe D. Hanebeck},
  title   = {{Methods for Deterministic Approximation of Circular Densities}},
  pages   = {138--156},
  volume  = {11},
  number  = {2},
  url     = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol11_2_3.pdf},
  year    = {2016},
  month   = dec,
  journal = {Journal of Advances in Information Fusion}
}

@article{JAIF16_Pfaff,
  author  = {Florian Pfaff and Gerhard Kurz and Uwe D. Hanebeck},
  title   = {{Multivariate Angular Filtering Using Fourier Series}},
  pages   = {206--226},
  volume  = {11},
  number  = {2},
  url     = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/JAIF_Vol11_2_7.pdf},
  year    = {2016},
  month   = dec,
  journal = {Journal of Advances in Information Fusion}
}


@incollection{MunozMorcillo2016,
  author    = {Jes{\'{u}}s {Munoz Morcillo} and Florian Faion and Antonio Zea and Uwe D. Hanebeck and Caroline Y. {Robertson-von Trotha}},
  booktitle = {Space and Time Visualisation},
  editor    = {{Boştenaru Dan}, Maria and Crăciun, Cerasella},
  isbn      = {978-3-319-24942-1},
  pages     = {173--191},
  pdf       = {STV16_Morcillo.pdf},
  publisher = {Springer International Publishing},
  title     = {{e-Installation: Synesthetic Documentation of Media Art via Telepresence Technologies}},
  year      = {2016}
}

@inproceedings{MFI16_Kurz,
  author    = {Gerhard Kurz and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Discrete Recursive Bayesian Filtering on Intervals and the Unit Circle}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  pdf       = {MFI16_Kurz.pdf},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {Various applications necessitate the estimation of
               quantities defined on intervals or the unit circle, which can
               also be parameterized as an interval. These applications include
               estimation of joint angles that are either limited to a certain
               range or that are 360-degree-periodic. For this purpose, we
               consider two approaches based on discretizing the state space
               that use fundamentally different density representations. We
               show how prediction and measurement update for systems with
               nonlinear dynamics and nonlinear measurement models can
               be performed in each representation. In particular, we discuss
               the choices that go into designing discrete filters, which are
               sometimes taken for granted. A thorough comparison and a
               numerical evaluation of both approaches show the advantages
               and disadvantages of each method.}
}

@inproceedings{MFI16_Zea,
  author    = {Antonio Zea and Florian Faion and Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{Exploiting Negative Measurements for Tracking Star-Convex Extended Objects}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {ABSTRACT},
  pdf       = {MFI16_Zea.pdf}
}

@inproceedings{MFI16_Faion,
  author    = {Florian Faion and Antonio Zea and Benjamin Noack and Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{Camera- and IMU-based Pose Tracking for Augmented Reality}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  pdf       = {MFI16_Faion.pdf}
}

@inproceedings{MFI16_Maier,
  author    = {Georg Maier and Florian Pfaff and Christoph Pieper and Robin Gruna and Benjamin Noack and Harald Kruggel-Emden and Thomas L{\"{a}}ngle and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and J{\"{u}}rgen Beyerer},
  title     = {{Fast Multitarget Tracking via Strategy Switching for Sensor-Based Sorting}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {State-of-the-art sensor-based sorting systems provide
               solutions to sort various products according to quality
               aspects. Such systems face the challenge of an existing delay
               between perception and separation of the material. To reliably
               predict an object's position when reaching the separation
               stage, information regarding its movement needs to be derived.
               Multitarget tracking offers approaches through which this
               can be achieved. However, processing time is typically limited
               since the sorting decision for each object needs to be derived
               sufficiently early before it reaches the separation stage. In this
               paper, an approach for multitarget tracking in sensor-based
               sorting is proposed which supports establishing an upper bound
               regarding processing time required for solving the measurement
               to track association problem. To demonstrate the success of
               the proposed method, experiments are conducted for data-sets
               obtained via simulation of a sorting system. This way, it
               is possible to not only demonstrate the impact on required
               runtime but also on the quality of the association.},
  pdf       = {MFI16_Maier.pdf}
}

@inproceedings{MFI16_Noack,
  author    = {Benjamin Noack and Joris Sijs and Uwe D. Hanebeck},
  title     = {{Algebraic Analysis of Data Fusion with Ellipsoidal Intersection}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {For decentralized fusion problems, ellipsoidal intersection has been proposed as an efficient fusion rule
               that provides less conservative results as compared to the well-know covariance intersection method. Ellipsoidal intersection
               relies on the computation of a common estimate that is shared by the estimates to be fused. In this paper,
               an algebraic reformulation of ellipsoidal intersection is discussed that circumvents the computation of the common estimate.
               It is shown that ellipsoidal intersection corresponds to an internal ellipsoidal approximation of the intersection of covariance ellipsoids.
               An interesting result is that ellipsoidal intersection can be computed with the aid of the Bar-Shalom/Campo fusion formulae.
               This is achieved by assuming a specific correlation structure between the estimates to be fused.},
  pdf       = {MFI16_Noack.pdf}
}

@inproceedings{MFI16_Pfaff,
  author    = {Florian Pfaff and Christoph Pieper and Georg Maier and Benjamin Noack and Harald Kruggel-Emden and Robin Gruna and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  title     = {{Simulation-Based Evaluation of Predictive Tracking for Sorting Bulk Materials}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {Multitarget tracking problems arise in many real-world
               applications. The performance of the utilized algorithm
               strongly depends both on how the data association problem is
               handled and on the suitability of the motion models employed.
               Especially the motion models can be hard to validate. Previously,
               we have proposed to use multitarget tracking to improve optical
               belt sorters. In this paper, we evaluate both the suitability of
               our model and the tracking and then of our entire system
               incorporating the image processing component via the use of
               highly realistic numerical simulations. We first assess the model
               using noise-free measurements generated by the simulation and
               then evaluate the entire system by using synthetically generated
               image data.},
  pdf       = {MFI16_Pfaff.pdf}
}

@inproceedings{MFI16_Steinbring,
  author    = {Jannik Steinbring and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Semi-Analytic Progressive Gaussian Filtering}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {As an alternative to Kalman filters and particle filters, recently the progressive Gaussian filter (PGF) was proposed for estimating the state of discrete-time stochastic nonlinear dynamic systems. Like Kalman filters, the estimate of the PGF is a Gaussian distribution, but like particle filters, its measurement update works directly with the likelihood function in order to avoid the inherent linearization of the Kalman filters. However, compared to particle filters, the PGF allows for much faster state estimation and circumvents the severe problem of particle degeneracy by gradually transforming its prior Gaussian distribution into a posterior one. In this paper, we further enhance the estimation quality and runtime of the PGF by proposing a semi-analytic measurement update applicable to likelihood functions that only depend on a subspace of the system state. In fact, the proposed semi-analytic measurement update is not limited to the PGF and can be used by any nonlinear state estimator as long as its state estimate is Gaussian, e.g., the Gaussian particle filter.},
  pdf       = {MFI16_Steinbring_PGF.pdf}
}

@inproceedings{MFI16_Steinbring2,
  author    = {Jannik Steinbring and Christian Mandery and Florian Pfaff and Florian Faion and Tamim Asfour and Uwe D. Hanebeck},
  title     = {{Real-Time Whole-Body Human Motion Tracking Based on Unlabeled Markers}},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
  year      = {2016},
  address   = {Baden-Baden, Germany},
  month     = sep,
  abstract  = {In this paper, we present a novel online approach for tracking whole-body human motion based on unlabeled measurements of markers attached to the body. For that purpose, we employ a given kinematic model of the human body including the locations of the attached markers. Based on the model, we apply a combination of constrained sample-based Kalman filtering and multi-target tracking techniques: 1) joint constraints imposed by the human body are satisfied by introducing a parameter transformation based on periodic functions, 2) a global nearest neighbor (GNN) algorithm computes the most likely one-to-one association between markers and measurements, and 3) multiple hypotheses tracking (MHT) allows for a robust initialization that only requires an upright standing user. Evaluations clearly demonstrate that the proposed tracking provides highly accurate pose estimates in real-time, even for fast and complex motions. In addition, it provides robustness to partial occlusion of markers and also handles unavoidable clutter measurements.},
  pdf       = {MFI16_Steinbring_Tracking.pdf}
}

@inproceedings{DEM16_Pieper,
  title     = {{Numerical Investigation of Optical Sorting Using the Discrete Element Method}},
  author    = {Christoph Pieper and Harald Kruggel-Emden and Siegmar Wirtz and Viktor Scherer and Florian Pfaff and Benjamin Noack and Uwe D. Hanebeck and Georg Maier and Robin Gruna and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  booktitle = {Proceedings of the 7th International Conference on Discrete Element Methods (DEM7)},
  series    = {Springer Proceedings in Physics},
  year      = {2016},
  address   = {Dalian, China},
  month     = aug,
  pdf       = {DEM7_Pieper.pdf},
  abstract  = {Automated optical sorting systems are important devices in the growing field of bulk solids handling. The initial sorter calibration and the precise optical sorting of many materials is still very time consuming and difficult. A numerical model of an automated optical belt sorter is presented in this study. The sorter and particle interaction is described with the Discrete Element Method (DEM) while
               the separation phase is considered in a post processing step. Different operating parameters and their influence on sorting quality are investigated. In addition, two models for detecting and predicting the particle movement between the detection point and the separation step are presented and compared, namely a conventional line scan camera model and a new approach combining an area scan camera model with particle tracking.}
}

@inproceedings{Fusion16_Steinbring,
  author    = {Jannik Steinbring and Benjamin Noack and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{Optimal Sample-Based Fusion for Distributed State Estimation}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Steinbring.pdf},
  abstract  = {In this paper, we present a novel approach to
               optimally fuse estimates in distributed state estimation for linear
               and nonlinear systems. An optimal fusion requires the knowledge
               of the correct correlations between locally obtained estimates.
               The naive and intractable way of calculating the correct
               correlations would be to exchange information about every processed
               measurement between all nodes. Instead, we propose to obtain
               the correct correlations by keeping and processing a small set
               of deterministic samples on each node in parallel to the actual
               local state estimation. Sending these samples in addition to the
               local state estimate to the fusion center allows for correctly
               reconstructing the desired correlations between all estimates.
               In doing so, each node does not need any information about
               measurements processed on other nodes. We show the optimality
               of the proposed method by means of tracking an extended object
               in a multi-camera network.}
}

@inproceedings{Fusion16_Baum,
  author    = {Marcus Baum and Shishan Yang and Uwe D. Hanebeck},
  title     = {{The Kernel-SME Filter with False and Missing Measurements}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  abstract  = {The recently proposed Kernel-SME filter for multiobject
               tracking is a further development of the Symmetric
               Measurement Equation (SME) idea introduced by Kamen in
               the 1990s. The Kernel-SME constructs a symmetric, i.e., permutation
               invariant, measurement equation by transforming the
               measurements to a kernel mixture function. This transformation
               is scalable to a large number of objects and allows for deriving an
               efficient closed-form Gaussian filter based on the Kalman filter
               formulas. This work shows how the Kernel-SME approach can
               systematically incorporate false and missing measurements.},
  pdf       = {Fusion16_Kernel-SME_Filter.pdf}
}

@inproceedings{Fusion16_Chlebek,
  author    = {Christof Chlebek and Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{Progressive Gaussian Filter Using Importance Sampling and Particle Flow}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Chlebek.pdf},
  abstract  = {We propose a novel progressive Gaussian filter for nonlinear stochastic systems. A Gaussian approximation of the posterior is computed without an explicit assumption of a linear relation between the system state and the measurement. This allows for better quality of the estimation compared to Kalman filters for nonlinear problems like the EKF or UKF. In this work, we use the progressive filter framework, which gradually incorporates information of a measurement into the state estimate
               by considering a flow of probability mass from the prior to the posterior state estimate. We propose a novel particle flow by utilizing a simple linear model. This model predicts the movement of single particles over the course of the filter progression. The predicted trajectory is corrected using importance sampling and moment matching. The proposed method is evaluated in comparison with other state-of-the-art nonlinear Bayesian filters.}
}

@inproceedings{Fusion16_Duan,
  author    = {Zhansheng Duan and Qi Zhou and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  title     = {{Extended Kernel-Based Location Fingerprinting in Wireless Sensor Networks}},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  abstract  = {Fingerprinting localization is to estimate a mobile
               terminal's location using its online received signal strength (RSS)
               measurement and offline RSS database originated from multiple
               access points (APs). Kernel-based fingerprinting localization is
               such a competitive algorithm. However, all training data need to
               be considered in its offline model learning stage. This render high
               risks for overfitting. To alleviate this, we suggest to apply clustering
               to the localization region of interest first and then use kernalbased
               fingerprinting localization for each cluster. A byproduct of
               clustering is that the computational load for each cluster is also
               significantly reduced. To further reduce the computational load
               within each cluster, we also suggest to apply principal component
               compression to the raw RSS measurements to reduce their
               dimensionality. The rationale for applying principal component
               compression is that the distributions of the RSS measurements
               at all calibration points (CPs) within each cluster will be
               more similar after clustering. Performance evaluation using both
               simulated data and real data show that the extended kernelbased
               fingerprinting localization using clustering and principal
               component compression have better location estimation accuracy
               and less computational load.},
  pdf       = {Fusion16_Duan_Fingerprinting.pdf}
}

@inproceedings{Fusion16_Faion,
  author    = {Florian Faion and Maxim Dolgov and Antonio Zea and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  title     = {{Closed-form Bias Reduction for Shape Estimation with Polygon Models}},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  abstract  = {We look at the task of estimating the parameters
               of a geometric constraint from noisy points in 2D. The classical
               approach of minimizing the Euclidean distance error between
               points and constraint generally yields biased estimates for
               non-linear constraints and higher noise levels. To deal with this
               issue, the expected distribution of the distance error can be
               explicitly incorporated in the estimator. However, for piecewise
               linear constraints, e.g., polygons, only computationally
               demanding sampling-based approaches are available. We propose two
               major contributions in order to resolve this issue. First, we
               derive closed-form expressions for the probability density of the
               signed distance between noisy points and a polygon angle. Second,
               based on this result, we develop a bias reduction method for
               polygons, which can be calculated in closed-form as well. We
               demonstrate that the quality of our approach can compete with
               its sampling-based alternatives, but only demands a fraction of
               their computational cost.},
  pdf       = {Fusion16_Faion.pdf}
}

@inproceedings{Fusion16_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck and Roland Siegwart},
  title     = {{Optimal Quantization of Circular Distributions}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Quantization.pdf},
  abstract  = {In this work, a new method for approximating
               circular probability distributions by a mixture of weighted
               discrete samples is proposed. Particularly, the wrapped normal
               distribution, the von Mises distribution, and the Bingham distribution
               are considered. The approximation approach is based on
               formulating a quantizer and a global optimality measure, which
               can be optimized directly. Furthermore, a relationship between
               the Bingham distribution and the von Mises distribution are
               formulated showing that it is sufficient to approximate a von
               Mises distribution with suitably chosen parameters in order to
               obtain an optimal approximation of the Bingham distribution.
               The resulting approximation is of particular interest for filtering
               applications, because the involved optimality measure gives rise to
               a general error estimate in propagation of uncertainties through
               nontrivial functions in the circular domain.}
}

@inproceedings{Fusion16_Hanebeck,
  author    = {Uwe D. Hanebeck and Martin Pander},
  title     = {{Progressive Bayesian Estimation with Deterministic Particles}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Hanebeck.pdf},
  abstract  = {This paper introduces an enhanced
               method for progressive Bayesian estimation based
               on a set of deterministic samples. The information
               of a given measurement is gradually introduced in
               order to avoid particle degeneration, which is usually
               encountered in standard particle filters. The main
               contribution of this paper is to derive a new method
               for exploiting smoothness assumptions about the
               unknown underlying density function of the state.}
}

@inproceedings{Fusion16_Kurz-Control,
  author    = {Gerhard Kurz and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Progressive Closed-Loop Chance-Constrained Control}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Kurz-ChanceConstraints.pdf},
  abstract  = {Chance-constrained control is a difficult problem
               even if the considered system dynamics are linear. The difficulty
               stems from the facts that the chance constraints are difficult
               to evaluate and that the control law is nonlinear due to the
               constraints. In this paper, we present a novel approach to chanceconstrained
               control, where we solve the unconstrained control
               problem first and then use a progressive method to gradually
               introduce the chance constraints. This has significant advantages
               compared to traditional methods, because we do not require a
               feasible initial solution for the numerical optimization algorithm.
               Finally, we evaluate the novel method and compare it to an
               approach from literature.}
}

@inproceedings{Fusion16_Kurz,
  author    = {Gerhard Kurz and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Kullback-Leibler Divergence and Moment Matching for Hyperspherical Probability Distributions}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Kurz-KLD2.pdf},
  abstract  = {When approximating one probability density with
               another density, it is desirable to minimize the information loss
               of the approximation as quantified by, e.g., the Kullback–Leibler
               divergence (KLD). It has been known for some time that in the
               case of the Gaussian distribution, matching the first two moments
               of the original density yields the optimal approximation in terms
               of minimizing the KLD. In this paper, we will show that a similar
               property can be proven for certain hyperspherical probability
               distributions, namely the von Mises–Fisher and the Watson
               distribution. This result has profound implications for momentbased
               filtering on the unit hypersphere as it shows that momentbased
               approaches are optimal in the information-theoretic sense.}
}

@inproceedings{Fusion16_Noack,
  author    = {Benjamin Noack and Florian Pfaff and Marcus Baum and Uwe D. Hanebeck},
  title     = {{State Estimation Considering Negative Information with Switching Kalman and Ellipsoidal Filtering}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Noack.pdf},
  abstract  = {State estimation concepts like the Kalman filter heavily rely on potentially noisy sensor data.
               In general, the estimation quality depends on the amount of sensor data that can be exploited.
               However, missing observations do not necessarily impair the estimation quality but may also convey
               exploitable information on the system state. This type of information - noted as negative
               information - often requires specific measurement and noise models in order to take advantage of it.
               In this paper, a hybrid Kalman filter concept is employed that allows using both stochastic and
               set-membership representations of information. In particular, the latter representation is
               intended to account for negative information, which can often be easily described as a bounded
               set in the measurement space. Depending on the type of information, the filtering step of the
               proposed estimator adaptively switches between Gaussian and ellipsoidal noise representations.
               A target tracking scenario is studied to evaluate and discuss the proposed concept.}
}

@inproceedings{Fusion16_Pfaff,
  author    = {Florian Pfaff and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Nonlinear Prediction for Circular Filtering Using Fourier Series}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_nonlinearPred.pdf},
  abstract  = {While nonlinear filtering for circular quantities is
               closely related to nonlinear filtering on linear domains, the
               underlying manifold enables the development of novel filters that
               take advantage of the boundedness of the domain. Previously,
               we introduced Fourier filters that approximate the density or its
               square root using Fourier series. For these filters, we proposed
               filter steps for arbitrary likelihoods and prediction steps for
               the identity system model with additive noise. This paper adds
               the capability of handling arbitrary transition densities in the
               prediction step, which facilitates, e.g., the use of the filters for
               nonlinear systems with additive noise. In the evaluation, the new
               prediction steps for the Fourier filters outperform an SIR particle
               filter, a grid filter, and a nonlinear variant of the von Mises filter.}
}

@inproceedings{Fusion16_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Tracking Elongated Extended Objects Using Splines}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  pdf       = {Fusion16_Zea.pdf},
  address   = {Heidelberg, Germany},
  month     = jul
}

@article{PowTec16_Pieper,
  title    = {{Numerical Modeling of an Automated Optical Belt Sorter Using the Discrete Element Method}},
  author   = {Christoph Pieper and Georg Maier and Florian Pfaff and Harald Kruggel-Emden and Siegmar Wirtz and Robin Gruna and Benjamin Noack and Viktor Scherer and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer and Uwe D. Hanebeck},
  journal  = {Powder Technology},
  year     = {2016},
  month    = jul,
  abstract = {Optical sorters are important devices in the processing and handling
              of the globally growing material streams. The precise optical sorting of many
              bulk solids is still difficult due to the great technical effort necessary
              for transport and flow control. In this study, particle separation with an automated
              optical belt sorter is modeled numerically. The Discrete Element Method (DEM) is used
              to model the sorter and calculate the particle movement as well as
              particle – particle and particle – wall interactions. The particle ejection stage with
              air valves is described with the help of a MATLAB script utilizing particle movement information
              obtained with the DEM. Two models for predicting the particle movement between
              the detection and separation phase are implemented and compared. In the first model,
              it is assumed that the particles are moving with belt velocity and without any cross movements
              and a conventional line scan camera is used for particle detection. In the second model,
              a more sophisticated approach is employed where the particle motion is predicted with
              an area scan camera combined with a tracking algorithm. In addition, the influence of
              different operating parameters like particle shape or conveyor belt length on the
              separation quality of the system is investigated. Results show that numerical simulations
              can offer detailed insight into the operation performance of optical sorters and help to
              optimize operating parameters. The area scan camera approach was found to be superior to
              the standard line scan camera model in almost all investigated categories.},
  doi      = {10.1016/j.powtec.2016.07.018},
  url      = {https://dx.doi.org/10.1016/j.powtec.2016.07.018}
}

@inproceedings{ECC16_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Approximation of Stochastic Nonlinear Closed-Loop Feedback Control with Application to Miniature Walking Robots}},
  booktitle = {Proceedings of the 2016 European Control Conference (ECC 2016)},
  year      = {2016},
  address   = {Aalborg, Denmark},
  month     = jun,
  pdf       = {ECC16_Chlebek.pdf},
  abstract  = {We consider stochastic nonlinear time-variant systems
               with imperfect state information in the context of model
               predictive control. The optimal control performance can only be
               achieved by closed-loop policies that anticipate future behavior.
               However, the computation of these policies is in general not
               tractable due to the presence of the dual effect, i.e., the control
               actions not only influence the state but also the uncertainty
               of its estimate. Thus, we propose an approximation to closedloop
               control. We use a forward calculation approach, which is
               derived from an open-loop feedback (OLF) control setup, but
               implements the fundamental property of closed-loop control
               that future measurement feedback is considered in the optimization.
               By using a finite set of representative measurements,
               the feedback behavior is anticipated only on basis of current
               information. An optimization based on a continuation method
               implements an effective calculation to obtain a sequence of
               control inputs. The effectiveness of the presented approach is
               demonstrated by means of a miniature walking robot.}
}

@inproceedings{ECC16_Dolgov,
  author    = {Maxim Dolgov and Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Dynamic Compensation of Markov Jump Linear Systems without Mode Observation}},
  booktitle = {Proceedings of the 2016 European Control Conference (ECC 2016)},
  year      = {2016},
  address   = {Aalborg, Denmark},
  month     = jun,
  pdf       = {ECC16_Dolgov.pdf},
  abstract  = {In this paper, we address control of Markov Jump Linear Systems
               via dynamic output feedback without mode observation. Because the optimal
               nonlinear control law for this problem is intractable, we assume a linear
               controller. Under this assumption, the control law computation can be
               expressed in terms of an optimization problem that involves Bilinear Matrix
               Inequalities. Unfortunately, this problem is NP-hard. Thus, we propose an
               alternative iterative algorithm and demonstrate it in a numerical example.
               To the best of our knowledge, the paper presents the first solution of
               mode-independent dynamic compensation of Markov Jump Linear Systems.}
}

@article{JAIF16_Symmetric_S2KF_Steinbring,
  author   = {Jannik Steinbring and Martin Pander and Uwe D. Hanebeck},
  title    = {{The Smart Sampling Kalman Filter with Symmetric Samples}},
  year     = {2016},
  month    = jun,
  volume   = {11},
  number   = {1},
  pages    = {71--90},
  journal  = {Journal of Advances in Information Fusion},
  pdf      = {JAIF16_Steinbring.pdf},
  abstract = {Nonlinear Kalman Filters (KFs) are powerful and widely-used techniques when trying to estimate the hidden state of a stochastic nonlinear dynamic system. A novel sample-based KF is the Smart Sampling Kalman Filter (S²KF). It is based on deterministic Gaussian samples which are obtained from an offline optimization procedure. Although this sampling technique is quite effective, it does not preserve the point symmetry of the Gaussian distribution. In this paper, we overcome this issue by extending the S²KF with a new point-symmetric Gaussian sampling scheme to improve its estimation quality. Moreover, we also improve the numerical stability of the sample computation. This allows us to accurately approximate thousand-dimensional Gaussian distributions using tens of thousands of optimally placed and equally weighted samples. We evaluate the new symmetric S²KF by computing higher-order moments of standard normal distributions and investigate the estimation quality of the S²KF when dealing with symmetric measurement equations. Additionally, extended object tracking based on many measurements per time step is considered. This high-dimensional estimation problem shows the advantage of the S²KF being able to use an arbitrary number of samples independent of the state dimension, in contrast to other state-of-the-art sample-based Kalman Filters. Finally, other estimators also relying on the S²KF’s Gaussian sampling technique, e.g., the Progressive Gaussian Filter (PGF), will benefit from the new point-symmetric sampling as well.}
}

@article{SPL16_Kurz,
  author   = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  journal  = {IEEE Signal Processing Letters},
  title    = {{Unscented von Mises--Fisher Filtering}},
  year     = {2016},
  month    = apr,
  number   = {4},
  pages    = {463-467},
  volume   = {23},
  abstract = {We introduce the unscented von Mises–Fisher filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, e.g., when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, we propose a deterministic sampling scheme on the unit hypersphere, which allows us to handle nonlinear system and measurement models. The proposed approach can be seen as a hyperspherical variant of the unscented Kalman filter (UKF). The advantages of the novel method are shown by means of simulations.},
  doi      = {10.1109/LSP.2016.2529854},
  pdf      = {Kurz-UnscentedVMF.pdf}
}

@article{AES16_Kurz,
  author   = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title    = {{Recursive Bayesian Filtering in Circular State Spaces}},
  month    = mar,
  volume   = {31},
  number   = {3},
  pages    = {70-87},
  pdf      = {AES16_Kurz.pdf},
  journal  = {IEEE Aerospace and Electronic Systems Magazine},
  year     = {2016},
  abstract = {To facilitate recursive state estimation in the circular domain based on circular statistics, we introduce a general framework for estimation of a circular state based on different circular distributions. Specifically, we consider the wrapped normal (WN) distribution and the von Mises distribution. We propose an estimation method for circular systems with nonlinear system and measurement functions. This is achieved by relying on efficient deterministic sampling techniques. Furthermore, we show how the calculations can be simplified in a variety of important special cases, such as systems with additive noise, as well as identity system or measurement functions, which are illustrated using an example from aeronautics. We introduce several novel key components, particularly a distribution-free prediction algorithm, a new and superior formula for the multiplication of WN densities, and the ability to deal with nonadditive system noise. All proposed methods are thoroughly evaluated and compared with several state-of-the-art approaches.},
  doi      = {10.1109/MAES.2016.150083}
}

@article{TM16_Pfaff,
  title    = {{Improving Optical Sorting of Bulk Materials Using Sophisticated Motion Models}},
  author   = {Florian Pfaff and Christoph Pieper and Georg Maier and Benjamin Noack and Harald Kruggel-Emden and Robin Gruna and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  journal  = {tm -- Technisches Messen, De Gruyter},
  number   = {2},
  pages    = {77--84},
  volume   = {83},
  year     = {2016},
  month    = feb,
  abstract = {Visual properties are powerful features to
              reliably classify bulk materials, thereby allowing to detect
              defect or low quality particles. Optical belt sorters are
              an established technology to sort based on these properties,
              but they suffer from delays between the simultaneous
              classification and localization step and the subsequent
              separation step. Therefore, accurate models to predict the
              particles’ motions are a necessity to bridge this gap. In
              this paper, we explicate our concept to use sophisticated
              simulations to derive accurate models and optimize the
              flow of bulk solids via adjustments of the sorter design.
              This allows us to improve overall sorting accuracy and
              cost efficiency. Lastly, initial results are presented.},
  doi      = {/10.1515/teme-2015-0108},
  url      = {https://www.degruyter.com/view/j/teme.2016.83.issue-2/teme-2015-0108/teme-2015-0108.xml},
  pdf      = {TM16_Pfaff.pdf}
}

@article{TAC16_Gilitschenski,
  author   = {Igor Gilitschenski and Gerhard Kurz and Simon J. Julier and Uwe D. Hanebeck},
  title    = {{Unscented Orientation Estimation Based on the Bingham Distribution}},
  journal  = {IEEE Transactions on Automatic Control},
  number   = {1},
  pages    = {172-177},
  volume   = {61},
  doi      = {10.1109/TAC.2015.2423831},
  month    = jan,
  year     = {2016},
  abstract = {In this work, we develop a recursive filter to estimate
              orientation in 3D, represented by quaternions, using directional distributions.
              Many closed-form orientation estimation algorithms are based on
              traditional nonlinear filtering techniques, such as the extended Kalman
              filter (EKF) or the unscented Kalman filter (UKF). These approaches
              assume the uncertainties in the system state and measurements to be
              Gaussian-distributed. However, Gaussians cannot account for the periodic
              nature of the manifold of orientations and thus small angular errors
              have to be assumed and ad hoc fixes must be used. In this work,
              we develop computationally efficient recursive estimators that use the
              Bingham distribution. This distribution is defined on the hypersphere
              and is inherently more suitable for periodic problems. As a result,
              these algorithms are able to consistently estimate orientation even in the
              presence of large angular errors. Furthermore, handling of nontrivial
              system functions is performed using an entirely deterministic method
              which avoids any random sampling. A scheme reminiscent of the UKF
              is proposed for the nonlinear manifold of orientations. It is the first
              deterministic sampling scheme that truly reflects the nonlinear manifold
              of orientations.},
  pdf      = {TAC16_Gilitschenski.pdf}
}

@inproceedings{CDC15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Parameter Estimation for the Bivariate Wrapped Normal Distribution}},
  booktitle = {Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015)},
  year      = {2015},
  address   = {Osaka, Japan},
  month     = dec,
  abstract  = {Correlated uncertain angular quantities can be
               modeled using the bivariate wrapped normal distribution.
               In this paper, we focus on the problem of estimating the
               distribution's parameters from a given set of samples. For
               this purpose, we propose several new parameter estimation
               methods and compare them to estimation techniques found in
               literature. All methods are thoroughly evaluated in simulations.
               One of the novel methods is shown to combine the advantages
               of maximum likelihood estimation and moment-based methods,
               thus outperforming current state-of-the-art techniques.},
  pdf       = {CDC15_Kurz.pdf}
}

@inproceedings{CDC15_Dolgov,
  author    = {Maxim Dolgov and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Chance-constrained Model Predictive Control based on Box Approximations}},
  booktitle = {Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015)},
  year      = {2015},
  address   = {Osaka, Japan},
  month     = dec,
  abstract  = {In this paper, we consider finite-horizon predictive
               control of linear stochastic systems with chance constraints
               where the admissible region is a convex polytope. For this
               problem, we present a novel solution approach based on box
               approximations. The key notion of our approach consists of
               two steps. First, we apply a linear operation to the joint
               state probability density function such that its covariance is
               transformed into an identity matrix. This operation also defines
               the transformation of the state space and therefore, of the
               admissible polytope. Second, we approximate the admissible
               region from the inside using axis-aligned boxes. By doing so, we
               obtain a conservative approximation of the constraint violation
               probability virtually in closed form (the expression contains
               Gaussian error functions). The presented control approach is
               demonstrated in a numerical example.},
  pdf       = {CDC15_Dolgov.pdf}
}

@incollection{CRC15_Sijs,
  title     = {Event-Based Control and Signal Processing},
  author    = {Joris Sijs and Benjamin Noack and Mircea Lazar and Uwe D. Hanebeck},
  chapter   = {{Time-Periodic State Estimation with Event-Based Measurement Updates}},
  editor    = {Marek Miskowicz},
  pages     = {261--279},
  publisher = {CRC Press},
  year      = {2015},
  month     = nov,
  abstract  = {To reduce the amount of data transfers
               in networked systems, measurements can be taken at
               an event on the sensor value rather than periodically
               in time. Yet, this could lead to a divergence of estimation results when only the received measurement
               values are exploited in a state estimation procedure.
               A solution to this issue has been found by developing
               estimators that perform a state update at both the event
               instants as well as periodically in time: when an event
               occurs the estimated state is updated using the measurement received, while at periodic instants the update is
               based on knowledge that the sensor value lies within a
               bounded subset of the measurement space. Several solutions for event-based state estimation will be presented
               in this chapter, either based on stochastic representations
               of random vectors, on deterministic representations of
               random vectors or on a mixture of the two. All solutions aim to limit the required computational resources
               by deriving explicit solutions for computing estimation
               results. Yet, the main achievement for each estimation
               solution is that stability of the estimation results are (not directly) dependent on the employed event sampling
               strategy. As such, changing the event sampling strategy does not imply to change the event-based estimator
               as well. This aspect is also illustrated in a case study
               of tracking the distribution of a chemical compound
               effected by wind via a wireless sensor network.},
  url       = {https://doi.org/10.1201/b19013}
}

@article{TAES15_Kurz,
  author   = {Gerhard Kurz and Uwe D. Hanebeck},
  title    = {{Trigonometric Moment Matching and Minimization of the Kullback--Leibler Divergence}},
  journal  = {IEEE Transactions on Aerospace and Electronic Systems},
  number   = {1},
  pages    = {3480-3484},
  volume   = {51},
  doi      = {10.1109/TAES.2015.150406},
  month    = oct,
  year     = {2015},
  abstract = {We show an important property of the von Mises
              distribution on the unit circle. If we approximate an arbitrary
              circular distribution using a von Mises distribution, the result
              obtained by trigonometric moment matching also minimizes
              the Kullback–Leibler divergence (Theorem 1). This result is a
              justification for circular filtering algorithms based on trigonometric
              moment matching as the loss of information is minimized.
              Furthermore, we show that Theorem 1 does not hold for the
              wrapped normal distribution.},
  pdf      = {TAES15_Kurz.pdf}
}

@article{SPL15_Baum,
  author  = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title   = {{On Wasserstein Barycenters and MMOSPA Estimation}},
  journal = {IEEE Signal Processing Letters},
  year    = {2015},
  month   = oct,
  url     = {https://ieeexplore.ieee.org/document/7054438/},
  number  = {10},
  pages   = {1511-1515},
  volume  = {22},
  doi     = {10.1109/LSP.2015.2410217}
}

@inproceedings{SDF15_Faion,
  author    = {Florian Faion and Antonio Zea and Jannik Steinbring and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Recursive Bayesian Pose and Shape Estimation of 3D Objects Using Transformed Plane Curves}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)}},
  year      = {2015},
  address   = {Bonn, Germany},
  month     = oct,
  abstract  = {We consider the task of recursively estimating the
               pose and shape parameters of 3D objects based on noisy point
               cloud measurements from their surface. We focus on objects
               whose surface can be constructed by transforming a plane curve,
               such as a cylinder that is constructed by extruding a circle.
               However, designing estimators for such objects is challenging,
               as the straightforward distance-minimizing approach cannot
               observe all parameters, and additionally is subject to bias in
               the presence of noise. In this article, we first discuss these issues
               and then develop probabilistic models for cylinder, torus, cone,
               and an extruded curve by adapting related approaches including
               Random Hypersurface Models, partial likelihood, and symmetric
               shape models. In experiments with simulated data, we show that
               these models yield unbiased estimators for all parameters even
               in the presence of high noise.},
  pdf       = {SDF15_Faion.pdf}
}

@inproceedings{SDF15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Stochastic Sampling of the Hyperspherical von Mises--Fisher Distribution Without Rejection Methods}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)}},
  year      = {2015},
  address   = {Bonn, Germany},
  month     = oct,
  abstract  = {We propose a novel sampling algorithm for the
               von Mises–Fisher distribution on the unit hypersphere. Unlike
               previous works, we show a solution for an arbitrary number of
               dimensions without requiring rejection sampling. As a result, the
               proposed algorithm has a deterministic runtime. The key idea
               consists in applying the inversion method to a one-dimensional
               subproblem and analytically calculating the integral occurring in
               the distribution function. The proposed method is most efficient
               for odd numbers of dimensions. We compare the algorithm to a
               state-of-the-art rejection sampling method in simulations.},
  pdf       = {SDF15_Kurz-VMFSampling.pdf}
}

@inproceedings{MFI15_Baum,
  author    = {Marcus Baum and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Kalman Filter-based SLAM with Unknown Data Association using Symmetric Measurement Equations}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {This work investigates a novel method for dealing
               with unknown data associations in Kalman filter-based
               Simultaneous Localization and Mapping (SLAM) problems. The key
               idea is to employ the concept of Symmetric Measurement
               Equations (SMEs) in order to remove the data association
               uncertainty from the original measurement equation. Based
               on the resulting modified measurement equation, standard
               nonlinear Kalman filters can estimate the full joint state vector
               of the robot and landmarks without explicitly calculating data
               association hypotheses.},
  pdf       = {MFI15_Baum.pdf}
}

@inproceedings{MFI15_Faion,
  author    = {Florian Faion and Marcus Baum and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Depth Sensor Calibration by Tracking an Extended Object}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {In this paper, we propose a novel algorithm for automatically
               calibrating a network of depth sensors, based on a moving calibration object.
               The sensors may have non-overlapping fields of view in order to avoid interference.
               Two major challenges are discussed. First, depending on where the object is
               located relative to the sensor, the number and quality of the measurements strongly varies.
               Second, a single depth sensor observes the calibration object only from one side.
               Dealing with these challenges requires a simple calibration object as
               well as an algorithm that can deal with under-determined measurements of
               varying quality. A recursive Bayesian estimator is developed that determines the
               extrinsic parameters by measuring the surface of a moving cube with
               known pose. Our approach does not restrict the configuration of the network and
               requires no manual initialization or interaction.
               Ambiguities that are induced by the rotational cube symmetries are
               resolved by applying a multiple model approach. Besides synthetic evaluation we
               perform real data experiments and compare to state-of-the-art calibration.},
  pdf       = {MFI15_Faion.pdf}
}

@inproceedings{MFI15_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{A Stochastic Filter for Planar Rigid-Body Motions}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {This paper presents a novel algorithm for the
               estimation of planar rigid-body motions. It is based on using a
               probability distribution that is inherently defined on the non-
               linear manifold representing these motions and on proposing
               a deterministic sampling scheme that makes consideration of
               complicated system models possible. Furthermore, we show
               that the measurement update for a manifold equivalent to
               noisy direct measurements can be carried out in closed form.
               Thus, the resulting method avoids errors made due to local
               linearization and outperforms methods that wrongly assume
               Gaussian distributions, which we show by comparing the
               proposed filter to the UKF.},
  pdf       = {MFI15_Gilitschenski.pdf}
}

@inproceedings{MFI15_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Bayesian Fusion of Empirical Distributions Based on Local Density Reconstruction}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Fusing two random vectors is simple, when they are characterized by continuous probability functions.
               According to Bayes' law, fusion then consists of multiplying the two densities. When only empirical distributions
               are given and a resulting empirical distribution is desired, Bayes' law is no longer applicable. Obviously,
               fusion could now be performed by reconstructing the underlying continuous densities, subsequent multiplication,
               and sampling of the result. As this is overly complicated, our goal is to perform a direct Bayesian fusion of the
               two given empirical distributions. We devise a generalized multiplication procedure that mutually reweights appropriate
               points of one density by local density values of the other density. The density values are efficiently estimated locally
               by nearest neighbor operations. The method is symmetric in the sense that it uses points from both densities.},
  pdf       = {MFI15_Hanebeck.pdf}
}

@inproceedings{MFI15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Toroidal Information Fusion Based on the Bivariate von Mises Distribution}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Fusion of toroidal information, such as correlated
               angles, is a problem that arises in many fields ranging from
               robotics and signal processing to meteorology and bioinfor-
               matics. For this purpose, we propose a novel fusion method
               based on the bivariate von Mises distribution. Unlike most
               literature on the bivariate von Mises distribution, we consider
               the full version with matrix-valued parameter rather than
               a simplified version. By doing so. we are able to derive
               the exact analytical computation of the fusion operation. We
               also propose an efficient approximation of the normalization
               constant including an error bound and present a parameter
               estimation algorithm based on a maximum likelihood approach.
               The presented algorithms are illustrated through examples.},
  pdf       = {MFI15_Kurz.pdf},
  annote    = {<b>Nominee Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/Mfi2015_award_nominee.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{MFI15_Noack,
  author    = {Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{State Estimation for Ellipsoidally Constrained Dynamic Systems with Set-Membership Pseudo Measurements}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {In many dynamic systems, the evolution of the
               state is subject to specific constraints. In general, constraints
               cannot easily be integrated into the prediction-correction structure
               of the Kalman filter algorithm. Linear equality constraints
               are an exception to this rule and have been widely used and
               studied as they allow for simple closed-form expressions. A
               common approach is to reformulate equality constraints into
               pseudo measurements of the state to be estimated. However,
               equality constraints define deterministic relationships between
               state components which is an undesirable property in Kalman
               filtering as this leads to singular covariance matrices. A second
               problem relates to the knowledge required to identify and
               define precise constraints, which are met by the system state.
               In this article, ellipsoidal constraints are introduced that can
               be employed to model a bounded region, to which the system
               state is constrained. This concept constitutes an easy-to-use
               relaxation of equality constraints. In order to integrate ellipsoidal
               constraints into the Kalman filter structure, a generalized filter
               framework is utilized that relies on a combined stochastic and
               set-membership uncertainty representation.},
  pdf       = {MFI15_Noack.pdf}
}

@inproceedings{MFI15_Pfaff,
  author    = {Florian Pfaff and Marcus Baum and Benjamin Noack and Uwe D. Hanebeck and Robin Gruna and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  title     = {{TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Optical belt sorters are a versatile, state-of-the-art technology to sort
               bulk materials that are hard to sort based on only nonvisual properties. In
               this paper, we propose an extension to current optical belt sorters that
               involves replacing the line camera with an area camera to observe a wider
               field of view, allowing us to observe each particle over multiple time
               steps. By performing multitarget tracking, we are able to improve the
               prediction of each particle's movement and thus enhance the performance of
               the utilized separation mechanism. We show that our approach will allow
               belt sorters to handle new classes of bulk materials while improving cost
               efficiency. Furthermore, we lay out additional extensions that are made
               possible by our new paradigm},
  pdf       = {MFI15_Pfaff.pdf}
}

@inproceedings{MFI15_Steinbring,
  author    = {Jannik Steinbring and Marcus Baum and Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{A Closed-Form Likelihood for Particle Filters to Track Extended Objects with Star-Convex RHMs}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Modeling 2D extended targets with star-convex Random Hypersurface Models (RHMs) allows for
               accurate object pose and shape estimation. A star-convex RHM models the shape of an object with the aid of
               a radial function that describes the distance from the object center to any point on its boundary. However,
               up to now only linear estimators, i.e., Kalman Filters, are used due to the lack of a explicit likelihood function.
               In this paper, we propose a closed-form and easy to implement likelihood function for tracking extended targets
               with star-convex RHMs. This makes it possible to apply nonlinear estimators such as Particle Filters to estimate
               a detailed shape of a target. We compared the proposed likelihood against the usual Kalman filter approaches
               with tracking pose and shape of an airplane in 2D. The evaluations showed that the combination of the
               Progressive Gaussian Filter (PGF) and the new likelihood function delivers the best estimation performance
               and can outperform the usually employed Kalman Filters.},
  pdf       = {MFI15_Steinbring.pdf}
}

@inproceedings{MFI15_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Shape Tracking using Partial Information Models}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {One of the challenges in shape tracking is how
               to deal with associating measurements to sources in the shape,
               while also taking to account parameters such as shape curvature
               and noise characteristics. Partial Information Models (PIMs)
               introduce a new approach that addresses this issue. The idea
               is to reparametrize each measurement into two components,
               one which depends on the position of its source on the shape,
               and another which depends on how well it fits in the shape.
               This allows for the derivation of a partial likelihood which
               combines the strengths of probabilistic approaches and distance
               minimization techniques. We propose an implementation of
               PIMs using level-sets, which allow for a close approximation
               of the distribution of distances we expect for a given shape. In
               turn, this can be used to develop estimators that are highly
               robust against high noise and occlusions.},
  pdf       = {MFI15_Zea.pdf}
}

@article{SPL15_Reinhardt,
  author   = {Marc Reinhardt and Benjamin Noack and Pablo O. Arambel and Uwe D. Hanebeck},
  title    = {{Minimum Covariance Bounds for the Fusion under Unknown Correlations}},
  journal  = {IEEE Signal Processing Letters},
  volume   = {22},
  number   = {9},
  pages    = {1210--1214},
  month    = sep,
  year     = {2015},
  abstract = {One of the key challenges in distributed linear estimation
              is the systematic fusion of estimates. While the fusion gains
              that minimize the mean squared error of the fused estimate for
              known correlations have been established, no analogous statement
              could be obtained so far for unknown correlations. In this contribution,
              we derive the gains that minimize the bound on the true covariance
              of the fused estimate and prove that Covariance Intersection (CI)
              is the optimal bounding algorithm for two estimates under completely
              unknown correlations. When combining three or more variables, the
              CI equations are not necessarily optimal, as shown by a counterexample.},
  doi      = {10.1109/LSP.2015.2390417},
  url      = {https://dx.doi.org/10.1109/LSP.2015.2390417},
  pdf      = {SPL15_Reinhardt.pdf}
}

@incollection{CRC15_Noack,
  booktitle = {Multisensor Data Fusion: From Algorithms and Architectural Design to Applications},
  author    = {Benjamin Noack and Joris Sijs and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{Treatment of Dependent Information in Multisensor Kalman Filtering and Data Fusion}},
  editor    = {Hassen Fourati},
  pages     = {169--192},
  publisher = {CRC Press},
  year      = {2015},
  month     = aug,
  abstract  = {Distributed and decentralized processing and fusion of sensor data are becoming increasingly important. In view of the Internet of Things and the vision of ubiquitous sensing, designing and implementing multisensor state estimation algorithm have already become a key issue. A network of interconnected sensor devices is usually characterized by the idea to process and collect data locally and independently on the sensor nodes. However, this does not imply that the data are independent of each other, and the state estimation algorithms have to address possible interdependencies so as to avoid erroneous data fusion results.
               Dependencies among local estimates generally can be traced back to common sensor information and common process noise. A wide variety of Kalman filtering schemes allow for the treatment of dependent data in centralized, distributed, and decentralized networks of sensor nodes, but making the right choice is itself dependent upon analyzing and weighing up the different advantages and disadvantages. This chapter discusses different strategies to identify and treat dependencies among Kalman filter estimates while pointing out advantages and challenges.},
  url       = {https://doi.org/10.1201/b18851}
}


@inproceedings{Fusion15_Baum,
  author    = {Marcus Baum and Balakumar Balasingam and Peter Willett and Uwe D. Hanebeck},
  title     = {{OSPA Barycenters for Clustering Set-Valued Data}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We consider the problem of clustering set-valued observations, i.e., each
               observation is a set that consists of a finite number of real vectors. For this
               purpose, we develop a $k$-means algorithm that employs the OSPA distance for
               measuring the distance between sets. In particular, we introduce a novel
               alternating optimization algorithm for the OSPA barycenter of sets with
               varying cardinalities that is required for calculating cluster centroids
               efficiently. The benefits of clustering set-valued data with respect to the OSPA
               distance are illustrated by means of simulated experiments in the context of
               target tracking and recognition.},
  pdf       = {Fusion15_Baum.pdf}
}

@inproceedings{Fusion15_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control Based on Deterministic Scenario Generation}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We consider closed-loop feedback (CLF) stochastic model predictive control of
               nonlinear time-invariant systems with imperfect state information. In this class of
               control problems, future information feedback is considered in the decision making process,
               and thus, the effect of the control influencing the state uncertainty is taken into account.
               The main challenge in the solution is to find a good approximation to the arising stochastic
               dynamic programming problem, which is computationally not tractable. In this work, future
               information is considered in the form of conditional state probability densities. Thus,
               the objective is it to optimize the state and its uncertainty as a combined problem.
               We propose to discretize the state space by a novel scenario generation approach based on
               deterministic sampling. A distance based threshold determines the narrowness of the discretization.
               The dynamic programming problem is formulated such that the approximate cumulative control
               cost function can be explicitly evaluated offline. The online calculation consists of a
               one-step prediction and the interpolation of the explicit cost function in order to calculate
               the control input. The effectiveness of this novel method is presented by means of a simulation.},
  pdf       = {Fusion15_Chlebek.pdf}
}

@inproceedings{Fusion15_Faion,
  author    = {Florian Faion and Antonio Zea and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Partial Likelihood for Unbiased Extended Object Tracking}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {An extended object gives rise to several measurements that originate
               from unknown measurement sources on the object. In this paper, we
               consider the tracking and parameter estimation of extended objects that
               are modeled as a curve in 2D such as a circle or an ellipse. A standard
               model for such extended objects is to assume that the unknown
               measurement sources are uniformly distributed on the curve. We argue
               that the uniform distribution may not be the best choice in scenarios
               where the true distribution of the measurements significantly differs
               from a uniform distribution. Based on results from curve fitting and
               errors-in-variables models, we develop a partial likelihood that ignores
               the distribution of measurement sources and can be shown to outperform
               the likelihood for a uniform distribution in these scenarios. If the
               true measurement sources are in fact uniformly distributed, our new
               likelihood results in a slightly slower convergence but has the same
               asymptotic behavior.},
  pdf       = {Fusion15_Faion.pdf}
}

@inproceedings{Fusion15_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Non-Identity Measurement Models for Orientation Estimation Based on Directional Statistics}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We propose a novel measurement update procedure for orientation
               estimation algorithms that are based on directional statistics. This
               involves consideration of two scenarios, orientation estimation in the
               2D plane and orientation estimation in three-dimensional space. We make
               use of the von Mises distribution and the Bingham distribution in these
               scenarios. In the derivation, we discuss directional counterparts to the
               extended Kalman filter and a statistical-linearization-based filter. The
               newly proposed algorithm makes use of deterministic sampling and can be
               thought of as a directional variant of the measurement update that is
               used in well-known sample-based algorithms such as the unscented Kalman
               filter.},
  pdf       = {Fusion15_Gilitschenski.pdf}
}

@inproceedings{Fusion15_Hanebeck-AssociationFreeTracking,
  author    = {Uwe D. Hanebeck and Marcus Baum},
  title     = {{Association-Free Direct Filtering of Multi-Target Random Finite Sets with Set Distance Measures}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We consider association-free tracking of multiple targets without identities.
               The uncertain multi-target state and the uncertain measurements cannot be described
               by a random vector as this would imply a certain order. Instead, they are described by
               an unordered random finite set (RFS). Particle-based random finite set densities are used
               for characterizing the RFS in a simple and natural way. For recursive Bayesian filtering,
               optimal multi-target state estimates are calculated by systematically minimizing an appropriate
               set distance measure while directly operating on the particles. Although methods for calculating
               point estimates of random finite set densities based on appropriate distance measures are
               available in literature, the proposed recursive filtering is a novel contribution.},
  pdf       = {Fusion15_Hanebeck-AssociationFreeTracking.pdf}
}

@inproceedings{Fusion15_Hanebeck-Polytopes,
  author    = {Uwe D. Hanebeck and Maxim Dolgov},
  title     = {{Adaptive Lower Bounds for Gaussian Measures of Polytopes}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {In this paper, we address the problem of probability mass
               computation of a multivariate Gaussian contained within a polytope. This
               computation requires an evaluation of a multivariate definite integral
               of the Gaussian, whose solution is not tractable for higher dimensions
               in a reasonable amount of time. Thus, research concentrates on the
               derivation of approximate but sufficiently fast computation methods. We
               propose a novel approach that approximates the underlying integration
               domain, namely the polytope, using disjoint sectors such that the
               probability mass contained within the sectors is maximized. In order to
               derive our main algorithm, we first propose an approach to approximate
               volume computation of a polytope using disjoint sectors. This solution
               is then extended to the computation of the probability mass of a
               Gaussian contained within the polytope. The presented solution provides
               a lower bound on the true probability mass contained within the
               polytope. Because the initial optimization problem is highly nonlinear,
               we propose a greedy algorithm that splits the sectors with the highest
               probability mass.},
  pdf       = {Fusion15_Hanebeck-Polytopes.pdf}
}

@inproceedings{Fusion15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Heart Phase Estimation Using Directional Statistics for Robotic Beating Heart Surgery}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {Robotic beating heart surgery requires accurate information about the
               current state of the heart. For this purpose, it is of great importance
               to have a good estimate of the heart's current phase, which in essence
               corresponds to the percentage of the current heart cycle that has
               already passed. Estimation of the heart phase is a highly nontrivial
               problem as the heart motion is not exactly periodic. On the contrary, it
               varies slightly from beat to beat and changes in frequency over time. In
               order to derive a robust phase estimation algorithm, we rely on
               directional statistics, a subfield of statistics that deals with
               quantities that are inherently periodic, such as the phase of the
               beating heart. The proposed methods are evaluated on a real data set and
               shown to be superior to the state of the art.},
  pdf       = {Fusion15_Kurz.pdf}
}

@inproceedings{Fusion15_Noack,
  author    = {Benjamin Noack and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{Treatment of Biased and Dependent Sensor Data in Graph-based SLAM}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {A common approach to attack the simultaneous localization and mapping problem (SLAM)
               is to consider factor-graph formulations of the underlying filtering and estimation setup.
               While Kalman filter-based methods provide an estimate for the current pose of a robot and
               all landmark positions, graph-based approaches take not only the current pose into account
               but also the entire trajectory of the robot and have to solve a nonlinear least-squares
               optimization problem. Using graph-based representations has proven to be highly scalable
               and very accurate as compared with traditional filter-based approaches. However, biased
               measurements as well as unmodeled correlations can lead to a sharp deterioration in the
               estimation quality and hence require careful consideration. In this paper, a method to
               incorporate biased or dependent measurement information is proposed that can easily be
               integrated into existing optimization algorithms for graph-based SLAM. For biased sensor
               data, techniques from ellipsoidal calculus are employed to compute the corresponding
               information matrices. Dependencies among noise terms are treated by a generalization
               of the covariance intersection concept. The treatment of both biased and correlated
               sensor data rest upon the inflation of the involved error matrices. Simulations
               are used to discuss and evaluate the proposed method.},
  pdf       = {Fusion15_Noack.pdf}
}

@inproceedings{Fusion15_Pfaff,
  author    = {Florian Pfaff and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Multimodal Circular Filtering Using Fourier Series}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  pdf       = {Fusion15_Pfaff.pdf},
  abstract  = {Recursive filtering with multimodal likelihoods and transition
               densities on periodic manifolds is, despite the compact domain, still an
               open problem. We propose a novel filter for the circular case that performs
               well compared to other state-of-the-art filters adopted from linear
               domains. The filter uses a limited number of Fourier coefficients of the
               square root of the density. This representation is preserved throughout
               filter and prediction steps and allows obtaining a valid density at any
               point in time. Additionally, analytic formulae for calculating Fourier
               coefficients of the square root of some common circular densities are
               provided. In our evaluation, we show that this new filter performs well in
               both unimodal and multimodal scenarios while requiring only a reasonable
               number of coefficients.}
}

@inproceedings{Fusion15_Steinbring,
  author    = {Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{GPU-Accelerated Progressive Gaussian Filtering with Applications to Extended Object Tracking}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even
               for non-graphic related applications. The field of nonlinear state estimation is no exception here.
               Particle Filters have already been successfully ported to GPUs. In this paper,
               we propose a GPU-accelerated variant of the Progressive Gaussian Filter (PGF). This allows
               us to combine the advantages of the particle flow with the ability to process thousands of
               measurements at once in order to improve state estimation quality. To get a meaningful
               comparison between its CPU and GPU variants, we additionally propose a likelihood for tracking
               a sphere and its extent in 3D based on noisy point measurements. The likelihood considers the
               physical relationship between sensor, measurement, and sphere to best exploit the information of the received measurements.
               We evaluate the GPU implementation of the PGF using the proposed likelihood in combination with tens of thousands of measurements.
               Although the CPU implementation fully exploits parallelization techniques such as SSE and OpenMP, the GPU-accelerated PGF
               reaches speedups over 20 and real-time tracking can nearly be achieved.},
  pdf       = {Fusion15_Steinbring.pdf}
}

@inproceedings{Fusion15_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Exploiting Clutter: Negative Information for Enhanced Extended Object Tracking}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even
               for non-graphic related applications. The field of nonlinear state estimation is no exception here.
               Particle Filters have already been successfully ported to GPUs. In this paper,
               we propose a GPU-accelerated variant of the Progressive Gaussian Filter (PGF). This allows
               us to combine the advantages of the particle flow with the ability to process thousands of
               measurements at once in order to improve state estimation quality. To get a meaningful
               comparison between its CPU and GPU variants, we additionally propose a likelihood for tracking
               a sphere and its extent in 3D based on noisy point measurements. The likelihood considers the
               physical relationship between sensor, measurement, and sphere to best exploit the information of the received measurements.
               We evaluate the GPU implementation of the PGF using the proposed likelihood in combination with tens of thousands of measurements.
               Although the CPU implementation fully exploits parallelization techniques such as SSE and OpenMP, the GPU-accelerated PGF
               reaches speedups over 20 and real-time tracking can nearly be achieved.},
  pdf       = {Fusion15_Zea.pdf}
}

@inproceedings{ACC15_Dolgov,
  author    = {Maxim Dolgov and J\"{o}rg Fischer and Uwe D. Hanebeck},
  title     = {Infinite-Horizon Sequence-based Networked Control without Acknowledgments},
  booktitle = {Proceedings of the 2015 American Control Conference (ACC 2015)},
  year      = {2015},
  month     = jul,
  address   = {Chicago, Illinois, USA},
  pdf       = {ACC2015_DolgovFischer.pdf},
  abstract  = {In this paper, we consider infinite-horizon networked LQG control over multipurpose networks that do not provide acknowledgments
               (UDP-like networks). The information communicated over the networks experiences transmission delays and losses that are modeled as stochastic
               processes. In oder to mitigate the delays and losses in the controller-actuator channel, the controller transmits sequences of predicted control inputs
               in addition to the current control input. To be able to mitigate delays and losses in the feedback channel, the estimator computes the estimate using
               M last measurements. In this scenario, the separation principle does not hold and the optimal control law is in general nonlinear. However, assuming
               that the controller and the estimator are linear and possess constant gains, we are able to derive the control law. The presented control law is
               evaluated by means of simulations.}
}

@inproceedings{ACC15_Kurz,
  author    = {Gerhard Kurz and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Nonlinear Stochastic Model Predictive Control in the Circular Domain}},
  booktitle = {Proceedings of the 2015 American Control Conference (ACC 2015)},
  year      = {2015},
  month     = jul,
  address   = {Chicago, Illinois, USA},
  pdf       = {ACC2015_KurzDolgov.pdf},
  abstract  = {In this paper, we present an open-loop Stochastic
               Model Predictive Control (SMPC) method for discrete-time
               nonlinear systems whose state is defined on the unit circle.
               This modeling approach allows considering systems that include
               periodicity in a more natural way than standard approaches
               based on linear spaces. The main idea of this work is twofold:
               (i) we model the quantities of the system, i.e., the state, the
               measurements, and the noises, directly as circular quantities
               described by circular probability densities, and (ii) we apply
               deterministic sampling given in closed form to represent the
               occurring densities. The latter allows us to make the prediction
               required for solution of the SMPC problem tractable. We
               evaluate the proposed control scheme by means of simulations.}
}

@article{JAIF15_Faion,
  author   = {Florian Faion and Antonio Zea and Marcus Baum and Uwe D. Hanebeck},
  title    = {{Symmetries in Bayesian Extended Object Tracking}},
  journal  = {Journal of Advances in Information Fusion},
  url      = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/433_1_art_9_24888.pdf},
  pages    = {13--30},
  volume   = {10},
  number   = {1},
  month    = jun,
  year     = {2015},
  abstract = {In this work, we exploit geometric symmetries in extended
              objects in order to improve Bayesian tracking algorithms that use
              Spatial Distribution Models, Greedy Association Models as used in
              curve fitting, and Random Hypersurface Models. The key idea is to
              describe symmetric objects by solely modeling the non-redundant
              part of the shape, while the remainder of the shape follows from
              symmetry. Following this idea, we develop simplified versions for
              the three models that take advantage of the symmetry. Exploiting
              symmetries yields two major benefits. First, complex symmetric
              shapes can be equivalently represented by a fraction of the original
              shape parameters. Second, when using sample-based filters, such as
              the widely used Unscented Kalman Filter, symmetry yields a higher
              effective sample resolution. It is worth mentioning that estimating
              even simple objects such as a stick, which only have one reflectional
              symmetry, can be significantly improved.}
}


@article{TSP15_Baum,
  author   = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title    = {{Polynomial-Time Algorithms for the Exact MMOSPA Estimate of a Multi-Object Probability Density Represented by Particles}},
  journal  = {IEEE Transactions on Signal Processing},
  year     = {2015},
  pages    = {2476 -- 2484},
  number   = {10},
  month    = may,
  url      = {https://ieeexplore.ieee.org/document/7041175/},
  doi      = {10.1109/TSP.2015.2403292},
  abstract = {In multi-object estimation, the traditional minimum mean squared error (MMSE) objective
              is unsuitable: a simple permutation of object identities can turn a very good estimate into
              what is apparently a very bad one. Fortunately, a criterion tailored to sets-minimization of
              the mean optimal sub-pattern assignment (MMOSPA)-has recently evolved. Aside from special cases,
              exact MMOSPA estimates have seemed difficult to compute. But in this work we present the first
              exact polynomial-time algorithms for calculating the MMOSPA estimate for probability densities
              that are represented by particles. The key insight is that the MMOSPA estimate can be found by means
              of enumerating the cells of a hyperplane arrangement, which is a traditional problem from computational
              geometry. Although the runtime complexity is still high for the general case, efficient algorithms are
              obtained for two special cases, i.e., (i) two targets with arbitrary state dimensions and (ii) an
              arbitrary number of one-dimensional targets.}
}

@article{AT15_Hanebeck,
  author   = {Uwe D. Hanebeck},
  title    = {{Optimal Reduction of Multivariate Dirac Mixture Densities}},
  journal  = {at -- Automatisierungstechnik, Oldenbourg Verlag},
  volume   = {63},
  number   = {4},
  pages    = {265--278},
  year     = {2015},
  month    = apr,
  doi      = {10.1515/auto-2015-0005},
  url      = {https://dx.doi.org/10.1515/auto-2015-0005},
  abstract = {This paper is concerned with the optimal approximation of a given multivariate Dirac mixture,
              i.e., a density comprising weighted Dirac distributions on a continuous domain, by a Dirac mixture
              with a reduced number of components. The parameters of the approximating density are calculated
              by numerically minimizing a smooth distance measure, a generalization of the well-known
              Cramer–von Mises-Distance to the multivariate case. This generalization is achieved by
              defining an alternative to the classical cumulative distribution, the Localized Cumulative
              Distribution (LCD), as a smooth characterization of discrete random quantities (on continuous domains).
              The resulting approximation method provides the basis for various efficient nonlinear
              estimation and control methods.}
}

@article{JAIF14_Steinbring-S2KF,
  author   = {Jannik Steinbring and Uwe D. Hanebeck},
  title    = {{LRKF Revisited: The Smart Sampling Kalman Filter (S2KF)}},
  journal  = {Journal of Advances in Information Fusion},
  volume   = {9},
  number   = {2},
  pages    = {106--123},
  year     = {2014},
  month    = dec,
  url      = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/441_1_art_11_17020.pdf},
  abstract = {An accurate Linear Regression Kalman Filter
              (LRKF) for nonlinear systems called Smart Sampling Kalman
              Filter (S²KF) is introduced. In order to get a better understanding
              of this new filter, a general introduction to Nonlinear Kalman
              Filters based on statistical linearization and LRKFs is given.
              The S²KF is based on a new low-discrepancy Dirac mixture
              approximation of Gaussian densities. This approximation com-
              prises an arbitrary number of optimally and deterministically
              placed samples in the relevant regions of the state space, so
              that the filter resolution can be adapted to either achieve high-
              quality results or to meet computational constraints. The S²KF
              contains the UKF with equally weighted samples as a special case
              when using the same amount of samples. With an increasing
              number of samples, the new filter converges to the (typically
              unfeasible) exact analytic statistical linearization. Hence, the
              S²KF can be seen as the ultimate generalization of all LRKFs
              such as the UKF, sigma-point filters, higher-order variants etc.,
              as it homogeneously covers the state space with a freely chosen
              number of samples. It is evaluated against state-of-the-art LRKFs
              by performing nonlinear prediction and extended target tracking.}
}

@article{JAIF14_Kurz-Bingham,
  author   = {Gerhard Kurz and Igor Gilitschenski and Simon Julier and Uwe D. Hanebeck},
  title    = {{Recursive Bingham Filter for Directional Estimation Involving 180 Degree Symmetry}},
  journal  = {Journal of Advances in Information Fusion},
  number   = {2},
  pages    = {90--105},
  volume   = {9},
  year     = {2014},
  month    = dec,
  pdf      = {JAIF13_Kurz-Bingham.pdf},
  abstract = {This work considers filtering of uncertain data
              defined on periodic domains, particularly the circle and the
              manifold of orientations in 3D space. Filters based on the Kalman
              filter perform poorly in this directional setting as they fail to take
              the structure of the underlying manifold into account. We present
              a recursive filter based on the Bingham distribution, which is
              defined on the considered domains. The proposed filter can be
              applied to circular filtering problems with 180 degree symmetry
              and to estimation of orientations in three dimensional space.
              It is easily implemented using standard numerical techniques
              and suitable for real-time applications. We evaluate our filter
              in a challenging scenario and compare it to a Kalman filtering
              approach adapted to the particular setting.}
}

@inproceedings{CDC14_Baum,
  author    = {Marcus Baum and Peter Willet and Uwe D. Hanebeck},
  title     = {{MMOSPA-based Track Extraction in the PHD Filter -- A Justification for k-Means Clustering}},
  booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
  year      = {2014},
  address   = {Los Angeles, California, USA},
  abstract  = {Displaying tracks is an essential part of a multi-
               target tracking system. Recently, it was proposed to extract
               tracks with respect to the Optimal Sub-Pattern Assignment
               (OSPA) metric, i.e., the traditionally used squared error loss
               is replaced with an OSPA loss, which leads to the so-called
               Minimum Mean OSPA (MMOSPA) estimate. So far, work
               concentrated on traditional trackers that maintain probability
               densities for the targets. In this paper, we aim at extracting
               the MMOSPA estimate from a Probability Hypothesis Density
               (PHD) as used within the PHD filter. We elaborate that the PHD
               in general does not contain enough information to determine
               the exact MMOSPA estimate. However, we then show that if
               the loss function has a specific form, it is indeed possible to
               extract point estimates from a PHD that are optimal w.r.t. the
               underlying unknown random finite set. We discuss two specific
               loss functions that fulfill this condition and are potentially close
               to the OSPA loss, a nearest neighbor loss and a kernel distance
               loss. It turns out that track extraction based on the nearest
               neighbor loss can be performed with the well-known k-means
               algorithm. Simulations show when the estimates based on the
               nearest neighbor and the kernel loss are close to the MMOSPA
               estimate.},
  pdf       = {CDC14_Baum.pdf},
  month     = dec
}

@inproceedings{CDC14_Dolgov,
  author    = {Maxim Dolgov and J\"{o}rg Fischer and Uwe D. Hanebeck},
  title     = {{Sequence-based LQG Control with Linear Integral Constraints over Stochastic Networks}},
  booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
  year      = {2014},
  address   = {Los Angeles, California, USA},
  abstract  = {In this paper, we consider sequence-based LQG control
               of stochastic linear systems with linear integral state and
               input constraints over networks subject to stochastic packet
               delays and losses. For this scenario, we derive a
               novel closed-loop optimal control law that consists of a
               feedback and a feedforward term. The feedback term depends
               linearly on the state estimate, while the feedforward term
               depends on the initial system state and the constraint functions.
               The control law can be partially given in closed-form that
               can be precomputed offline, and a numerical part which
               demands a solution of a quadratic optimization procedure.
               The number of the decision variables corresponds to the
               number of constraints. The presented control law is
               evaluated by means of a Monte-Carlo simulation.},
  pdf       = {CDC14_Dolgov.pdf},
  month     = dec
}

@inproceedings{CDC14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Bivariate Angular Estimation Under Consideration of Dependencies Using Directional Statistics}},
  booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
  year      = {2014},
  address   = {Los Angeles, California, USA},
  abstract  = {Estimation of angular quantities is a widespread
               issue, but standard approaches neglect the true topology of the
               problem and approximate directional with linear uncertainties.
               In recent years, novel approaches based on directional statistics
               have been proposed. However, these approaches have been
               unable to consider arbitrary circular correlations between
               multiple angles so far. For this reason, we propose a novel
               recursive filtering scheme that is capable of estimating multiple
               angles even if they are dependent, while correctly describing
               their circular correlation. The proposed approach is based on
               toroidal probability distributions and a circular correlation
               coefficient. We demonstrate the superiority to a standard
               approach based on the Kalman filter in simulations.},
  pdf       = {CDC14_Kurz.pdf},
  month     = dec
}

@inproceedings{SDF14_Faion,
  author    = {Florian Faion and Antonio Zea and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Bayesian Estimation of Line Segments}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  year      = {2014},
  address   = {Bonn, Germany},
  month     = oct,
  pdf       = {SDF14_Faion.pdf},
  abstract  = {A popular approach when tracking extended objects
               with elongated shapes, such as ships or airplanes, is to
               approximate them as a line segment. Despite its simple shape,
               the distribution of measurement sources on a line segment can
               be characterized in many radically different ways. The spectrum
               ranges from Spatial Distribution Models that assume a distinct
               probability for each individual source, to Greedy Association
               Models as used in curve fitting, which do not assume any
               distribution at all. In between these border cases, Random
               Hypersurface Models assume a distribution over subsets of all
               sources. In this paper, we compare Bayesian estimators based
               on these different models. We point out their advantages and
               disadvantages and evaluate their performance by means of
               illustrative examples with synthetic and real data using a Linear
               Regression Kalman Filter.}
}

@inproceedings{SDF14_Gilitschenski,
  author    = {Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{A Direct Method for Checking Overlap of Two Hyperellipsoids}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  year      = {2014},
  address   = {Bonn, Germany},
  month     = oct,
  pdf       = {SDF14_Gilitschenski.pdf},
  abstract  = {In this work, we propose a method for checking
               whether two arbitrary-dimensional hyperellipsoids overlap without
               making use of any optimization or root-finding methods.
               This is achieved by formulating an overlap condition as a
               polynomial root counting problem, which can be solved directly.
               The addressed challenges involve the inversion of a polynomial
               matrix using a direct method. The proposed approach extends one
               of our earlier results, which was restricted to certain combinations
               of ellipsoids and yields a fixed run-time for a fixed problem
               dimensionality. Thus, for the first time, an algorithm for checking
               overlap of arbitrary hyperellipsoids is proposed that can be
               evaluated in closed form. That is, in the absence of cut-off errors,
               the proposed method yields an exact result after a finite number
               of steps.}
}

@inproceedings{SDF14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Efficient Evaluation of the Probability Density Function of a Wrapped Normal Distribution}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  year      = {2014},
  address   = {Bonn, Germany},
  abstract  = {The wrapped normal distribution arises when the
               density of a one-dimensional normal distribution is wrapped
               around the circle infinitely many times. At first look, evaluation
               of its probability density function appears tedious as an infinite
               series is involved. In this paper, we investigate the evaluation
               of two truncated series representations. As one representation
               performs well for small uncertainties, whereas the other performs
               well for large uncertainties, we show that in all cases a small
               number of summands is sufficient to achieve high accuracy.},
  pdf       = {SDF14_Kurz.pdf},
  month     = oct
}

@inproceedings{SDF14_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  title     = {{Tracking Extended Objects using Extrusion Random Hypersurface Models}},
  year      = {2014},
  address   = {Bonn, Germany},
  month     = oct,
  abstract  = {As sensor resolution increases, the accuracy and robustness
               of tracking algorithms can be improved by incorporating
               more information about the shape of the target object. This raises
               the need for simple and robust shape models capable of describing
               detailed objects. In this paper we propose an approach based on
               Random Hypersurface Models that interprets target shapes as
               scaled extrusions. This is achieved by combining projection-based
               models with probabilistic approaches, integrating the strengths
               of both mechanisms. As extruded shapes such as bottles, boxes,
               or containers can be extensively found in everyday situations,
               this approach can be applied for tracking in a large variety of
               environments.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/SDF14_best_paper_award.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {SDF14_Zea.pdf}
}

@inproceedings{MFI14_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{Efficient Bingham Filtering based on Saddlepoint Approximations}},
  booktitle = {Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2014)},
  year      = {2014},
  address   = {Beijing, China},
  abstract  = {In this paper, we discuss computational efficiency
               of a recursive estimator using the Bingham Distribution. The
               Bingham distribution is defined directly on the unit hypersphere.
               As such, it is able to describe both large and small uncertainties
               in a unified framework. In order to tackle the challenging
               computation of the normalization constant, we propose a method
               using its Saddlepoint approximations and an approximate MLE
               based on the Gauss-Newton method. In a set of simulation
               experiments, we demonstrate that the Bingham filter not only
               outperforms both Kalman and particle filters, but can also be
               implemented efficiently.},
  pdf       = {MFI2014_Gilitschenski.pdf},
  month     = sep
}

@inproceedings{MFI14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{The Partially Wrapped Normal Distribution for SE(2) Estimation}},
  booktitle = {Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2014)},
  year      = {2014},
  address   = {Beijing, China},
  abstract  = {We introduce a novel probability distribution on the
               group of rigid motions SE(2) and we refer to this distribution
               as the partially wrapped normal distribution. Describing probabilities
               on the SE(2) is of interest in a wide range of areas, for
               example robotics, autonomous vehicles, or information fusion. We
               derive some important properties of this novel distribution and
               derive an estimation scheme for its parameters based on moment
               matching. Furthermore, we provide a comparison to a recently
               published approach based on the Bingham distribution, and show
               that there are complementary advantages and disadvantages of
               the two approaches.},
  pdf       = {MFI2014_Kurz-PWN.pdf},
  month     = sep
}

@inproceedings{MFI14_Reinhardt,
  author    = {Marc Reinhardt and Sanjeev Kulkarni and Uwe D. Hanebeck},
  title     = {{Generalized Covariance Intersection based on Noise Decomposition}},
  booktitle = {Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2014)},
  year      = {2014},
  address   = {Beijing, China},
  abstract  = {In linear decentralized estimation, several nodes
               concurrently aim to recursively estimate the state of a common
               phenomenon by means of local measurements and data exchanges
               subject to limited knowledge. In this contribution, an efficient
               algorithm for consistent estimation in sensor networks under
               Kalman filter assumptions is derived. The main theorems generalize
               Covariance Intersection by means of an explicit consideration
               of individual noise terms. We apply the results to linear
               decentralized estimation and obtain covariance bounds with a
               scalable precision between the exact covariances and the bounds
               provided by Covariance Intersection subject to computation and
               communication effort.},
  pdf       = {MFI2014_Reinhardt.pdf},
  month     = sep
}

@inproceedings{CURAC14_Kurz,
  author    = {Gerhard Kurz and Genevi{\`{e}}ve Foley and P{\'{e}}ter Heged{\"{u}}s and G{\'{a}}bor Szab{\'{o}} and Uwe D. Hanebeck},
  title     = {{Evaluation of Image Stabilization Methods in Robotic Beating Heart Surgery}},
  booktitle = {13. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC14)},
  year      = {2014},
  address   = {Munich, Germany},
  month     = sep,
  abstract  = {In cardiopulmonary bypass surgery, it is beneficial to avoid the use of a heart-lung machine and perform beating heart
               surgery instead. This is a difficult task even for the most skilled surgeons. To eliminate the risks associated with cardio-
               pulmonary bypass on a beating heart, a motion compensation system can be used. We place markers on the heart surface,
               which we can use to track the complex heart motion and to produce still footage of the heart surface by applying one of
               several stabilization algorithms to eliminate the motion. We compare six different stabilization algorithms, affine, B-
               spline, piecewise linear and three types of radial basis functions. In this paper, we evaluate the results using three eval-
               uation methods, pixel intensity average difference, optical flow, and stabilized marker tracking. All of these show a sig-
               nificant reduction in motion after stabilization, especially for interpolation-based stabilization methods as opposed to the
               affine approximation. We discuss advantages and disadvantages of the different evaluation methods.},
  pdf       = {CURAC14_Kurz.pdf}
}

@inproceedings{IFAC14_Fischer,
  author    = {J{\"{o}}rg Fischer and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Optimal Sequence-Based Tracking Control over Unreliable Networks}},
  booktitle = {Proceedings of the 19th IFAC World Congress (IFAC 2014)},
  year      = {2014},
  address   = {Cape Town, South Africa},
  abstract  = {In networked control systems, sequence-based controllers are used to compensate
               for transmission delays and losses in unreliable data networks. For this purpose, the controller
               sends not only the current control input to the actuator but also a sequence of predicted control
               inputs. The additional inputs can be used when subsequent transmissions get delayed or lost.
               In this paper, the sequence-based method is applied to the problem of trajectory tracking over
               an unreliable network and an optimal sequenced-based tracking controller is derived. The main
               advantage of the presented approach is that future information on the reference trajectory can
               optimally be embedded in the predicted control sequences. Furthermore, the controller can be
               implemented offline. An interesting result is that the optimal controller can still be separated
               into a feedback part and a feedforward part (as in standard optimal tracking control) despite of
               both the unreliable network and the sequence-based method. The performance of the derived
               tracking controller is demonstrated by Monte Carlo simulations with an inverted pendulum.},
  month     = aug,
  pdf       = {IFAC14_Fischer.pdf}
}

@inproceedings{IFAC14_Hanebeck,
  author    = {Uwe D. Hanebeck and Anders Lindquist},
  title     = {{Moment-based Dirac Mixture Approximation of Circular Densities}},
  booktitle = {Proceedings of the 19th IFAC World Congress (IFAC 2014)},
  year      = {2014},
  address   = {Cape Town, South Africa},
  abstract  = {Given a circular probability density function, called the true probability density
               function, the goal is to find a Dirac mixture approximation based on some circular moments of
               the true density. When keeping the locations of the Dirac points fixed, but almost arbitrarily
               located, we are applying recent results on the circulant rational covariance extension problem
               to the problem of calculating the weights. For the case of simultaneously calculating optimal
               locations, additional constraints have to be deduced from the given density. For that purpose,
               a distance measure for the deviation of the Dirac mixture approximation from the true density
               is derived, which then is minimized while considering the moment conditions as constraints.
               The method is based on progressive numerical minimization, converges quickly and gives well-
               distributed Dirac mixtures that fulfill the constraints, i.e., have the desired circular moments.},
  month     = aug,
  pdf       = {IFAC14_Hanebeck.pdf}
}

@inproceedings{IFAC14_Noack,
  author    = {Benjamin Noack and Joris Sijs and Uwe D. Hanebeck},
  title     = {{Fusion Strategies for Unequal State Vectors in Distributed Kalman Filtering}},
  booktitle = {Proceedings of the 19th IFAC World Congress (IFAC 2014)},
  year      = {2014},
  address   = {Cape Town, South Africa},
  abstract  = {Distributed implementations of state estimation algorithms generally have in
               common that each node in a networked system computes an estimate on the entire global
               state. Accordingly, each node has to store and compute an estimate of the same state vector
               irrespective of whether its sensors can only observe a small part of it. In particular, the task of
               monitoring large-scale phenomena renders such distributed estimation approaches impractical
               due to the sheer size of the corresponding state vector. In order to reduce the workload of the
               nodes, the state vector to be estimated is subdivided into smaller, possibly overlapping parts.
               In this situation, fusion does not only refer to the computation of an improved estimate but also
               to the task of reassembling an estimate for the entire state from the locally computed estimates
               of unequal state vectors. However, existing fusion methods require equal state representations
               and, hence, cannot be employed. For that reason, a fusion strategy for estimates of unequal
               and possibly overlapping state vectors is derived that minimizes the mean squared estimation
               error. For the situation of unknown cross-correlations between local estimation errors, also a
               conservative fusion strategy is proposed.},
  month     = aug,
  pdf       = {IFAC14_Noack.pdf}
}

@inproceedings{Fusion14_Ajgl,
  author    = {Ji{\v{r}}{\'{i}} Ajgl and Miroslav {\v{S}}imandl and Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Covariance Intersection in State Estimation of Dynamical Systems}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {The Covariance Intersection algorithm linearly
               combines estimates when the cross-correlations between their
               errors are unknown. It provides a fused estimate and an upper
               bound of the corresponding mean square error matrix. The
               weights of the linear combination are designed in order to
               minimise the upper bound. This paper analyses the optimal
               weights in relation to state estimation of dynamical systems.
               It is shown that the use of the optimal upper bound in a
               standard recursive filtering does not lead to optimal upper bounds
               in subsequent processing steps. Unlike the fusion under full
               knowledge, the fusion under unknown cross-correlations can fuse
               the same information differently, depending on the independent
               information that will be available in the future.},
  pdf       = {Fusion14_Ajgl.pdf}
}

@inproceedings{Fusion14_Duan,
  author    = {Zhansheng Duan and Xiao-Rong Li and Uwe D. Hanebeck},
  title     = {{Multi-sensor Distributed Estimation Fusion Using Minimum Distance Sum}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In multi-sensor distributed estimation fusion, local
               estimation errors are correlated in general. Two extreme ways to
               handle this correlation is either to ignore them completely or to
               have them fully considered. There is another case in the middle:
               it admits the existence of the correlation, but does not know how
               large it is. A sensible way is to set up an optimality criterion
               and optimize it over all possible such correlations. This work
               is a new development in the third class. First, a new general
               objective function is introduced, which is the minimum sum
               of statistical distances between the fused density and the local
               posterior densities. Then it is shown that the new criterion leads
               to a convex optimization problem if the Kullback-Leibler (KL)
               divergence is used as the statistical distance between assumed
               Gaussian densities. It is found that although the analytically
               obtained fused estimate using the new criterion differs from
               the simple convex combination rule only in mean squared error
               (MSE) by a scaling factor N (the number of sensors used), it is
               pessimistic semi-definite in MSE. Numerical examples illustrate
               the effectiveness of the proposed distributed fuser by comparing
               with several widely used distributed fusers.},
  pdf       = {Fusion14_Duan.pdf}
}

@inproceedings{Fusion14_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Pole-based Distance Measure for Change Detection in Linear Dynamic Systems}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In this work, we derive a distance measure for
               the detection of changes in the behavior of linear dynamic
               single-input-single-output (SISO) systems based on input-output
               data. The distance is calculated as a function of the system
               poles, which are directly estimated from the given data. Poles
               represent a system as a set and have no identities, which is
               analogous to the nature of association-free multi-target tracking.
               This motivates the application of set distances known from
               multi-target tracking, namely the optimal subpattern assign-
               ment (OSPA) distance. Thus, the OSPA distance as well as a
               modification, the MAX-OSPA distance, are formulated as pole-
               distances between dynamic systems. In this formulation, the
               OSPA distance finds the optimal assingment by minimizing over
               the sum of distances between poles. The MAX-OSPA chooses
               an optimal assignment by minimizing the maximum distance
               between two poles. The proposed distances are evaluated in
               several simulations comparing the deterministic OSPA and MAX-
               OSPA to a state-of-the-art metric for autoregressive-moving-
               average (ARMA) processes, as well as OSPA and MAX-OSPA
               using the direct pole estimation and a two step-pole estimation
               utilizing recursive ARX (AutoRegressive model with eXogenous
               input) system identification.},
  pdf       = {Fusion14_Chlebek.pdf}
}

@inproceedings{Fusion14_Faion,
  author    = {Florian Faion and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Reducing Bias in Bayesian Shape Estimation}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {This work considers the problem of estimating the
               parameters of an extended object based on noisy point observations
               from its boundary. The intention is to explore relationships
               between common approaches by breaking them down into their
               basic assumptions within the Bayesian framework. In doing so,
               we find that distance-minimizing curve fitting algorithms can be
               modeled by using a special Spatial Distribution Model, where
               the source distribution is approximated by a greedy one-to-one
               association of points to sources on the shape boundary. Based on
               this insight, we explore the origin of the estimation bias, which is
               a well-known issue of curve fitting algorithms. Furthermore, we
               derive a general scheme to alleviate its effect for arbitrary shapes,
               as well as for non-isotropic noise. This procedure is shown to be
               a generalization of related special solutions.},
  pdf       = {Fusion14_Faion.pdf}
}

@inproceedings{Fusion14_GilitschenskiKurz,
  author    = {Igor Gilitschenski and Gerhard Kurz and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{A New Probability Distribution for Simultaneous Representation of Uncertain Position and Orientation}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {This work proposes a novel way to represent uncertainty
               on the Lie group of rigid-body motions in the plane. This is
               achieved by using dual quaternions for representation of a planar
               rigid-body motion and proposing a probability distribution from
               the exponential family of distributions that inherently respects the
               underlying structure of the representation. This is particularly
               beneficial in scenarios involving strong measurement noise. A
               relationship between the newly proposed distributional model
               and the Bingham distribution is discussed. The presented results
               involve formulas for computation of the normalization constant,
               the mode, parameter estimation techniques, and a closed-form
               Bayesian measurement fusion.},
  annote    = {<b>Best Student Paper Award First Runner-Up</b> <a href="https://isas.iar.kit.edu/pdf/award/Fusion2014_best_student_paper_award_first_runner_up.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {Fusion14_GilitschenskiKurz.pdf}
}

@inproceedings{Fusion14_GilitschenskiSteinbring,
  author    = {Igor Gilitschenski and Jannik Steinbring and Uwe D. Hanebeck and Miroslav Simandl},
  title     = {{Deterministic Dirac Mixture Approximation of Gaussian Mixtures}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In this work, we propose a novel way to approximating
               mixtures of Gaussian distributions by a set of deterministically
               chosen Dirac delta components. This approximation is performed
               by adapting a method for approximating single Gaussian
               distributions to the considered case. The proposed method turns
               the approximation problem into an optimization problem by
               minimizing a distance measure between the Gaussian mixture
               and its Dirac mixture approximation. Compared to the simple
               Gaussian case, the minimization criterion is much more complex
               as multiple, non-standard Gaussian distributions have to be considered.},
  pdf       = {Fusion14_GilitschenskiSteinbring.pdf}
}

@inproceedings{Fusion14_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Sample Set Design for Nonlinear Kalman Filters viewed as a Moment Problem}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {For designing sample sets for nonlinear Kalman
               filters, i.e., Linear Regression Kalman Filters (LRKFs), a new
               method is introduced for approximating Gaussian densities by
               discrete densities, so called Dirac Mixtures (DMs). This
               approximating DM should maintain the mean and some higher-
               order moments and should homogeneously cover the support
               of the original density. Homogeneous approximations require
               redundancy, which means there are more Dirac components than
               necessary for fulfilling the moment constraints. Hence, some
               sort of regularization is required as the solution is no longer
               unique. Two types of regularizers are possible: The first type
               ensures smooth approximations, e.g., in a maximum entropy
               sense. The second type we pursue here ensures closeness of
               the approximating density to the given Gaussian. As standard
               distance measures are typically not well defined for discrete
               densities on continuous domains, we focus on shifting the mass
               distribution of the approximating density as close to the true
               density as possible. Instead of globally comparing the masses as
               in a previous paper, the key idea is to characterize individual
               Dirac components by kernel functions representing the spread
               of probability mass that is appropriate at a given location. A
               distance measure is then obtained by comparing the deviation
               between the true density and the induced kernel density. As a
               result, the approximation problem is converted to an optimization
               problem as we now minimize the distance under the desired
               moment constraints.},
  pdf       = {Fusion14_Hanebeck.pdf}
}

@inproceedings{Fusion14_Sijs,
  author    = {Joris Sijs and Leon Kester and Benjamin Noack},
  title     = {{A Study on Event Triggering Criteria for Estimation}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {To reduce the amount of data transfer in networked
               systems measurements are usually taken only when an event
               occurs rather than periodically in time. However, a fundamental
               assessment on the response of estimation algorithms receiving
               event sampled measurements is not available. This research
               presents such an analysis when new measurements are sampled
               at well-designed events and sent to a Luenberger observer.
               Conditions are then derived under which the estimation error
               is bounded, followed by an assessment of two event sampling
               strategies when the estimator encounters two different types of
               disturbances: an impulse and a step disturbance. The sampling
               strategies are compared via four performance measures, such as
               estimation-error and communication resources. The result is a
               clear insight of the estimation response in an event-based setup.},
  pdf       = {Fusion14_Sijs.pdf}
}

@inproceedings{Fusion14_KurzGilitschenski,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Deterministic Approximation of Circular Densities with Symmetric Dirac Mixtures Based on Two Circular Moments}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {Circular estimation problems arise in many applications and can be
               addressed with the help of circular distributions. In particular, the
               wrapped normal and von Mises distributions are widely used in the
               context of circular problems. To facilitate the development of nonlinear
               filters, a deterministic sample-based approximation of these
               distributions with a so-called wrapped Dirac mixture distribution is
               beneficial. We propose a new closed-form solution to obtain a symmetric
               wrapped Dirac mixture with five components based on matching the first
               two circular moments. The proposed method is superior to
               state-of-the-art methods, which only use the first circular moment to
               obtain three Dirac components, because a larger number of Dirac
               components results in a more accurate approximation.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/Fusion2014_best_paper_award.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {Fusion14_KurzGilitschenski.pdf}
}

@inproceedings{Fusion14_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{2D and 3D Image Stabilization for Robotic Beating Heart Surgery}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {Image stabilization is relevant for various industrial and medical
               applications. In particular, we consider the use of image stabilization
               in robotic beating heart surgery. A robot, which is remotely controlled
               by the surgeon, can automatically compensate for the motion of the
               beating heart. To give the surgeon the illusion of operating on a
               stationary heart, a stabilized image of the beating heart is shown to
               the surgeon. Image stabilization cancels the unwanted motion of the
               heart, but retains changes to color and texture, for example cuts on the
               heart surface. In this paper, stabilization is first considered as a 2D
               image transformation problem. Subsequently, it is extended to
               stabilization of a 3D point cloud or surface. The proposed algorithms
               are evaluated in both ex-vivo and in-vivo experiments. In the
               evaluation, the stabilization quality achievable with several common
               interpolation functions is compared.},
  pdf       = {Fusion14_Kurz.pdf}
}

@inproceedings{Fusion14_Noack,
  author    = {Benjamin Noack and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{On Nonlinear Track-to-track Fusion with Gaussian Mixtures}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {The problem of fusing state estimates is encountered in many network-based multi-sensor applications.
               The majority of distributed state estimation algorithms are designed to provide multiple estimates on the
               same state, and track-to-track fusion then refers to the task of combining these estimates. While linear
               fusion only requires the joint cross-covariance matrix to be known, dependencies between estimates in
               nonlinear estimation problems have to be represented by high-dimensional probability density functions.
               In general, storing and keeping track of nonlinear dependencies is too cumbersome. However, this paper
               demonstrates that estimates represented by Gaussian mixtures prove to be an important exception to this rule.
               The dependency structure can as well be characterized in terms of a higher-dimensional Gaussian mixture.
               The different processing steps of distributed nonlinear state estimation, i.e., prediction, filtering,
               and fusion, are studied in light of the joint density representation. The presented concept is complemented
               with different simpler suboptimal representations of the dependency structure between Gaussian mixture densities.},
  pdf       = {Fusion14_Noack.pdf}
}

@inproceedings{Fusion14_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Sanjeev Kulkarni and Uwe D. Hanebeck},
  title     = {{Distributed Kalman Filtering in the Presence of Packet Delays and Losses}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {Distributed Kalman filtering aims at optimizing an
               estimate at a fusion center based on information that is gathered
               in a sensor network. Recently, an exact solution based on local
               estimation tracks has been proposed and an extension to cope
               with packet losses has been derived. In this contribution, we
               generalize both algorithms to packet delays. The key idea is
               to introduce augmented measurement vectors in the sensors
               that permit the optimization of local filter gains according to
               time-dependent measurement capabilities at the fusion center.
               In the most general form, the algorithm provides optimized
               estimates in sensor networks with packets delays and losses. The
               precision depends on the actual arrival patterns, and the results
               correspond to those of the centralized Kalman filter when specific
               assumptions about the measurement capability are satisfied.},
  pdf       = {Fusion14_Reinhardt.pdf}
}

@inproceedings{Fusion14_Steinbring,
  author    = {Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{Progressive Gaussian Filtering Using Explicit Likelihoods}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In this paper, we introduce a new sample-based
               Gaussian filter. In contrast to the popular Nonlinear Kalman
               Filters, e.g., the UKF, we do not rely on linearizing the
               measurement model. Instead, we take up the Gaussian progressive
               filtering approach introduced by the PGF 42 but explicitly rely
               on likelihood functions. Progression means, we incorporate the
               information of a new measurement gradually into the state
               estimate. The advantages of this filtering method are on the one
               hand the avoidance of sample degeneration and on the other
               hand an adaptive determination of the number of likelihood
               evaluations required for each measurement update. By this
               means, less informative measurements can be processed quickly,
               whereas measurements containing much information automatically
               receive more emphasis by the filter. These properties allow
               the new filter to cope with the demanding problem of very narrow
               likelihood functions in an efficient way.},
  pdf       = {Fusion14_Steinbring.pdf}
}

@inproceedings{Fusion14_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Tracking Connected Objects Using Interacting Shape Models}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {As sensor resolution increases, estimators tracking
               extended objects benefit from being able to closely model the
               shape of the target. However, as more shape details are
               incorporated, this usually leads to increasingly complex estimators.
               A more useful approach is to describe these shapes as a combination
               of simpler shapes connected to each other. In this paper, we
               propose a modular approach to estimate these combined targets
               in function of their simpler components. This allows the
               characteristics of each component to be encapsulated, and permits
               the combination of multiple filtering techniques as required by
               each component shape. This approach can be applied to track
               combined objects in a large variety of environments, such as
               excavators, robotic arms, wagon trains, and many others.},
  pdf       = {Fusion14_Zea.pdf}
}

@inproceedings{ECC14_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Bayesian Approach to Direct Pole Estimation}},
  booktitle = {Proceedings of the 2014 European Control Conference (ECC 2014)},
  year      = {2014},
  month     = jun,
  address   = {Strasbourg, France},
  abstract  = {In this work, a solution to the direct pole identification problem
               of discrete-time autoregressive (AR) processes by general recursive
               Bayesian estimation is presented. The poles of the transfer function
               of an AR process are identified directly from the process output
               data. Without intermediate estimation of the AR coefficient, the
               AR process identification problem by means of its poles becomes nonlinear,
               and thus cannot be solved exactly. A practical solution by application
               of statistical linearization is given. The derived direct pole estimation
               algorithm by statistical linearization is given in closed-form and
               regression point based, by the so-called Linear Regression Kalman
               Filter (LRKF). Two realizations of the LRKF algorithm are tested,
               namely the Unscented Kalman Filter (UKF) for low computational complexity
               and thus, for high update rates, and the Smart Sampling Kalman Filter
               (S2KF) for high precision with faster convergence. Both, the UKF
               and S2KF are compared to the Adaptive Pole Estimation (APE), a solution
               by recursive nonlinear least squares minimizing the prediction error
               gradient.},
  pdf       = {ECC14_Chlebek.pdf}
}

@inproceedings{SAM14_Baum,
  author    = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title     = {{MMOSPA-based Direction-of-Arrival Estimation for Planar Antenna Arrays}},
  booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
  year      = {2014},
  month     = jun,
  address   = {A Coru\~{n}a, Spain},
  abstract  = {This work is concerned with direction-of-arrival
               (DOA) estimation of narrowband signals from multiple targets
               using a planar antenna array. We illustrate the shortcomings of
               Maximum Likelihood (ML), Maximum a Posteriori (MAP), and
               Minimum Mean Squared Error (MMSE) estimation, issues that
               can be attributed to the symmetry in the likelihood function that
               must exist when there is no information about labeling of targets.
               We proffer the recently introduced concept of Minimum Mean
               OSPA (MMOSPA) estimation that is based on the optimal subpattern
               assignment (OSPA) metric for sets and hence inherently
               incorporates symmetric likelihood functions.},
  pdf       = {SAM14_Baum.pdf}
}

@inproceedings{SAM14_Kurz,
  author    = {Gerhard Kurz and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Real-time Kernel-based Multiple Target Tracking for Robotic Beating Heart Surgery}},
  booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
  year      = {2014},
  month     = jun,
  address   = {A Coru\~{n}a, Spain},
  abstract  = {Performing surgery on the beating heart has significant advantages for
               the patient compared to traditional heart surgery on the stopped heart.
               A remote-controlled robot can be used to automatically cancel out the
               movement of the beating heart. This necessitates precise tracking of the
               heart surface. For this purpose, we track 24 identical artificial
               markers placed on the heart. This creates a data association problem,
               because it is not known which measurement was obtained from which
               marker. To solve this problem, we apply a multiple target tracking
               method based on a symmetric kernel transformation. This method allows
               efficient handling of the data association problem even for a reasonably
               large number of targets. We demonstrate how to implement this method
               efficiently. The proposed approach is evaluated on in-vivo data of a
               real beating heart surgery performed on a porcine beating heart.},
  pdf       = {SAM14_Kurz.pdf}
}

@inproceedings{SAM14_Zea,
  author    = {Antonio Zea and Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking Simplified Shapes Using a Stochastic Boundary}},
  booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
  year      = {2014},
  month     = jun,
  address   = {A Coru\~{n}a, Spain},
  abstract  = {When tracking extended objects, it is often the case that the shape of the
               target cannot be fully observed due to issues of visibility, artifacts, or
               high noise, which can change with time.
               In these situations, it is a common approach to model targets as simpler
               shapes instead, such as ellipsoids or cylinders.
               However, these simplifications cause information loss from the original
               shape, which could be used to improve the estimation results.
               In this paper, we propose a way to recover information from these lost
               details in the form of a stochastic boundary, whose parameters can be
               dynamically estimated from received measurements.
               The benefits of this approach are evaluated by tracking an object using
               noisy, real-life RGBD data.},
  pdf       = {SAM14_Zea.pdf}
}

@inproceedings{ACC14_Dolgov,
  author    = {Maxim Dolgov and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{State Estimation for Stochastic Hybrid Systems Based on Deterministic Dirac Mixture Approximation}},
  booktitle = {Proceedings of the 2014 American Control Conference (ACC 2014)},
  year      = {2014},
  month     = jun,
  address   = {Portland, Oregon, USA},
  abstract  = {In this paper, we consider state estimation for Stochastic Hybrid
               Systems (SHS). These are systems that possess both continuous-valued and
               discrete-valued dynamics. For SHS with nonlinear hybrid dynamics and/or
               non-Gaussian disturbances, state estimation can be implemented as an
               Interacting Multiple Model (IMM) particle filter. However, a
               disadvantage of particle filtering is the computational load caused by
               the large number of particles required for a sufficiently good
               estimation. We address this issue by first expressing the probability
               density that describes the state of the SHS as a collection of densities
               of the continuous-valued state only conditioned on the discrete-valued
               state. Then, we deterministically approximate these individual densities
               with Dirac mixtures. The employed approximation method places the
               particles so that a so called modified Cram\'{e}r-von Mises distance
               between the true and the approximated density is minimized.
               Deterministic approximation requires far less particles than the
               stochastic sampling used by particle filters. To avoid particle
               degeneration that can occur when a density is multiplied with the
               likelihood, the filter uses progressive density correction. The
               presented filter is demonstrated in a numerical maneuvering target
               tracking example.},
  pdf       = {ACC14_Dolgov.pdf}
}

@inproceedings{ACC14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{N}onlinear {M}easurement {U}pdate for {E}stimation of {A}ngular {S}ystems {B}ased on {C}ircular {D}istributions},
  booktitle = {Proceedings of the 2014 American Control Conference (ACC 2014)},
  year      = {2014},
  month     = jun,
  address   = {Portland, Oregon, USA},
  abstract  = {In this paper, we propose a novel progressive nonlinear measurement
               update for circular states. This generalizes our previously published
               circular filter that so far was limited to identity measurement
               equations. The new update method is based on circular distributions in
               order to capture the periodic properties of a circular system better
               than conventional approaches that rely on standard Gaussian
               distributions. Besides the progressive measurement update, we propose
               two additional measurement updates that are obtained by adapting
               traditional filters to the circular case. Simulations show the
               superiority of the proposed progressive approach.},
  pdf       = {ACC14_Kurz.pdf}
}

@article{JAIF14_Kurz,
  title    = {{D}ynamic {S}urface {R}econstruction by {R}ecursive {F}usion of {D}epth and {P}osition {M}easurements},
  author   = {Gerhard Kurz and Uwe D. Hanebeck},
  journal  = {Journal of Advances in Information Fusion},
  year     = {2014},
  month    = jun,
  url      = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/427_1_art_6_19271\%5B1\%5D.pdf},
  number   = {1},
  pages    = {13--26},
  volume   = {9},
  abstract = {Surface estimation can be performed based on position or depth measurements.
              We propose a method to fuse both types of measurements. Position measurements are obtained
              from landmarks on the surface, i.e., they are fixed to a certain point on the surface. In
              contrast, depth measurements reflect the depth measured along a line emanating from a depth
              camera and are not fixed to a position on the surface. The proposed approach uses a mixture
              of Cartesian and polar or spherical coordinate to treat both measurement types accordingly.
              By doing so, the uncertainties associated with the different measurement types are explicitly
              considered. The presented method represents the surface by a spline and is applicable to both
              2D and 3D applications. Surface estimation is considered as a recursive filtering problem and
              standard nonlinear filtering methods suchas the unscented Kalman filter can be used to obtain
              surface estimates. We show a thorough evaluation of the proposed approach in simulations.},
  pdf      = {JAIF14_Kurz.pdf}
}

@inproceedings{SPIE14_Baum,
  author    = {Marcus Baum and Peter Willet and Yaakov Bar-Shalom and Uwe D. Hanebeck},
  title     = {{Approximate Calculation of Marginal Association Probabilities using a Hybrid Data Association Model}},
  booktitle = {SPIE -- Signal and Data Processing of Small Targets 2014},
  address   = {Baltimore, Maryland, USA},
  month     = jun,
  year      = {2014},
  doi       = {10.1117/12.2053431},
  url       = {https://dx.doi.org/10.1117/12.2053431},
  abstract  = {The calculation of marginal association probabilities is
               the major computational bottleneck in the Joint Probabilistic
               Data Association Filter (JPDAF). In this paper, we investigate
               approximations for the marginal associations that simplify the
               (computational complex) original association model in order to
               obtain efficient algorithms. In this context, we first discuss
               the Bakhtiar-Alavi algorithm and the Linear Multitarget Integrated
               Probabilistic Data Association (LMIPDA) algorithm. Second, we
               propose a fast novel approximation that exploits systematic
               combinations of the JPDAF measurement model with the
               Probabilistic Multi-Hypothesis Tracker (PMHT) measurement model.
               The discussed methods are evaluated by means of a tracking
               scenario with a high number of closely-spaced targets.}
}

@inproceedings{CISS14_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Reconstruction of Joint Covariance Matrices in Networked Linear Systems}},
  year      = {2014},
  abstract  = {In this paper, a sample representation of the estimation error is
               utilized to reconstruct the joint covariance matrix in a distributed estimation
               system. The key idea is to sample uncorrelated and fully correlated noise
               according to different techniques at local estimators without knowledge about
               the processing of other nodes in the network. This way, the correlation between
               estimates is inherently linked to the representation of the corresponding
               sample sets. We discuss the noise processing, derive key attributes, and
               evaluate the precision of the covariance estimates.},
  booktitle = {Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014)},
  address   = {Princeton, New Jersey, USA},
  month     = mar,
  pdf       = {CISS14_Reinhardt.pdf}
}

@inproceedings{CISS14_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Kernel-based Deterministic Blue-noise Sampling of Arbitrary Probability Density Functions}},
  year      = {2014},
  abstract  = {This paper provides an efficient method for approximating a given
               continuous probability density function (pdf) by a Dirac mixture density. Optimal
               parameters are determined by systematically minimizing a distance measure. As
               standard distance measures are typically not well defined for discrete densities
               on continuous domains, we focus on shifting the mass distribution of the
               approximating density as close to the true density as possible. Instead of globally
               comparing the masses as in a previous paper, the key idea is to characterize
               individual Dirac components by kernel functions representing the spread of
               probability mass that is appropriate at a given location. A distance measure is
               then obtained by comparing the deviation between the true density and the induced
               kernel density. This new method for Dirac mixture approximation provides
               high-quality approximation results, can handle arbitrary pdfs, allows considering
               constraints for, e.g., maintaining certain moments, and is fast enough for online
               processing.},
  booktitle = {Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014)},
  address   = {Princeton, New Jersey, USA},
  month     = mar,
  pdf       = {CISS14_Hanebeck.pdf}
}

@incollection{SPP14_Fischer,
  author    = {J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{D}istributed and {N}etworked {M}odel {P}redictive {C}ontrol},
  booktitle = {{Control Theory of Digitally Networked Dynamic Systems}},
  publisher = {Springer International Publishing},
  year      = {2014},
  month     = jan,
  editor    = {Jan Lunze},
  pages     = {111--167},
  doi       = {10.1007/978-3-319-01131-8_4},
  isbn      = {978-3-319-01130-1},
  url       = {https://dx.doi.org/10.1007/978-3-319-01131-8_4},
  abstract  = {In this chapter, we consider the problem of controlling networked
               and distributed systems by means of model predictive control (MPC).
               The basic idea behind MPC is to repeatedly solve an optimal control
               problem based on a model of the system to be controlled. Every time
               a new measurement is available, the optimization problem is solved
               and the corresponding input sequence is applied until a new measurement
               arrives. As explained in the sequel, the advantages of MPC over other
               control strategies for networked systems are due to the fact that
               a model of the system is available at the controller side, which
               can be used to compensate for random bounded delays. At the same
               time, for each iteration of the optimization problem an optimal input
               sequence is calculated. In case of packet dropouts, one can reuse
               this information to maintain closed-loop stability and performance.},
  pdf       = {SPP14_Fischer.pdf}
}

@inproceedings{CDC13_Fischer,
  author    = {J{\"{o}}rg Fischer and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{On Stability of Sequence-Based LQG Control}},
  booktitle = {Proceedings of the 52st IEEE Conference on Decision and Control (CDC 2013)},
  year      = {2013},
  address   = {Florence, Italy},
  abstract  = {Sequence-based control is a well-established method applied in
               Networked Control Systems (NCS) to mitigate the effect of time-varying
               transmission delays and stochastic packet losses. The idea of this method
               is that the controller sends sequences of predicted control inputs to the
               actuator that can be applied in case a future transmission fails. In this paper,
               the stability properties of sequence-based LQG controllers are analyzed
               in terms of the boundedness of the long run average costs. On the one hand,
               we derive sufficient conditions, each for the boundedness and unboundedness
               of the costs. On the other hand, we give bounds on the minimal length of
               the control input sequence needed to stabilize a system.},
  pdf       = {CDC13_Fischer.pdf},
  month     = dec
}

@inproceedings{CURAC13_Kurz,
  author    = {Gerhard Kurz and Peter Hegedus and G{\'{a}}bor Szab{\'{o}} and Uwe D. Hanebeck},
  title     = {{Experimental Evaluation of Kinect and Inertial Sensors for Beating Heart Tracking}},
  booktitle = {12. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC13)},
  year      = {2013},
  address   = {Innsbruck, Austria},
  month     = nov,
  abstract  = {This paper investigates the use of Kinect depth sensors as well as
               inertial sensors in the context of beating heart surgery. In the past,
               various sensors have been used in attempts to track the beating heart,
               each with its own distinct set of advantages and disadvantages. With the
               availability of affordable structured-light depth sensors such as the
               Kinect and sufficiently small and low priced inertial sensors, the
               question of their suitability for beating heart tracking arises. We
               performed in-vivo experiments on a porcine heart in order to assess the
               feasibility of beating heart tracking based on these sensors.},
  pdf       = {CURAC13_Kurz.pdf}
}

@inproceedings{IPIN13_Kurz,
  author    = {Gerhard Kurz and Florian Faion and Uwe D. Hanebeck},
  title     = {{Constrained Object Tracking on Compact One-dimensional Manifolds Based on Directional Statistics}},
  booktitle = {Proceedings of the Fourth IEEE GRSS International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013)},
  year      = {2013},
  address   = {Montbeliard, France},
  abstract  = {In this paper, we present a novel approach for tracking objects whose
               movement is constrained to a compact one-dimensional manifold, for
               example a conveyer belt or a mobile robot whose movement is restricted
               to tracks. Standard approaches either ignore the constraint at first and
               retroactively move the estimate to lie on the manifold, or consider the
               tracking problem on a manifold but falsely assume a Gaussian
               distribution. Our method explicitly takes the actual topology into
               account from the beginning and relies on special types of probability
               distributions defined on the proper manifold. In particular, we consider
               objects moving along a closed one-dimensional track, for example an
               ellipse, a polygon, or similar closed shapes. This shape is transformed
               to a circle with a homeomorphism. Thus, we can apply a recursive
               circular filtering algorithm to the constrained tracking problem.
               Finally, the estimate is transformed back to the original manifold. We
               evaluate the proposed method in an experiment by tracking a toy train
               moving along a track and comparing the results to those of traditional
               approaches for this problem.},
  pdf       = {IPIN13_Kurz.pdf},
  month     = oct
}

@inproceedings{NecSys13_Dolgov,
  author    = {Maxim Dolgov and J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{Event-based LQG Control over Networks Subject to Random Transmission Delays and Packet Losses}},
  booktitle = {Proceedings of the 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2013)},
  year      = {2013},
  address   = {Koblenz, Germany},
  abstract  = {In Networked Control Systems (NCS), data networks not only
               limit the amount of information exchanged by system components but
               are also subject to stochastic packet delays and losses. In this
               paper, we present a controller that simultaneously addresses these
               problems by combining event-based and sequence-based control methods.
               At every time step, the proposed controller calculates a sequence
               of predicted control inputs and based on the expected future LQG
               costs decides whether it transmits the control sequence to the actuator.
               The proposed controller is evaluated with simulations.},
  pdf       = {NecSys13_Dolgov.pdf},
  month     = sep
}

@inproceedings{ECC13_Sijs,
  author    = {Joris Sijs and Uwe D. Hanebeck and Benjamin Noack},
  title     = {{An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation}},
  booktitle = {Proceedings of the 2013 European Control Conference (ECC 2013)},
  year      = {2013},
  address   = {Z{\"{u}}rich, Switzerland},
  abstract  = {State fusion is a method for merging multiple estimates of the same
               state into a single fused estimate. Dealing with multiple estimates
               is one of the main concerns in distributed state estimation, where
               an estimated value of the desired state vector is computed in each
               node of a networked system. Most solutions for distributed state
               estimation currently available assume that every node computes an
               estimate of the (same) global state vector. This assumption is impractical
               for systems observing large-area processes, due to the sheer size
               of the process state. A more feasible solutions is one where each
               node estimates a part of the global state vector, allowing different
               nodes in the network to have overlapping state elements. Although
               such an approach should be accompanied by a corresponding state fusion
               method, existing solutions cannot be employed as they merely consider
               fusion of two different estimates with equal state representations.
               Therefore, an empirical solution is presented for fusing two state
               estimates that have partially overlapping state elements. A justification
               of the proposed fusion method is presented, along with an illustrative
               case study for observing the temperature profile of a large rod,
               though a formal derivation is future research.},
  pdf       = {ECC13_Sijs.pdf},
  month     = jul
}

@inproceedings{Fusion13_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{The Kernel-SME Filter for Multiple Target Tracking}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {We present a novel method for tracking multiple targets, called Kernel-SME
               filter, that does not require an enumeration of measurement-to-target
               associations. This method is a further development of the symmetric
               measurement equation (SME) filter that removes the data association
               uncertainty of the original measurement equation with the help of
               a symmetric transformation. The key idea of the Kernel-SME filter
               is to define a symmetric transformation that maps the measurements
               to a Gaussian mixture function. This transformation is scalable to
               a large number of targets and allows for deriving a Gaussian state
               estimator that only has a cubic runtime complexity in the number
               of targets.},
  pdf       = {Fusion13_Baum.pdf},
  month     = jul
}

@inproceedings{Fusion13_Faion,
  author    = {Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Silhouette Measurements for Bayesian Object Tracking in Noisy Point Clouds}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {In this paper, we consider the problem of jointly tracking the pose
               and shape of objects based on noisy data from cameras and depth sensors.
               Our proposed approach formalizes object silhouettes from image data
               as measurements within a Bayesian estimation framework. Projecting
               object silhouettes from images back into space yields a visual hull
               that constrains the object. In this work, we focus on the 2D case.
               We derive a general equation for the silhouette measurement update
               that explicitly considers segmentation uncertainty of each pixel.
               By assuming a bounded error for the silhouettes, we can reduce the
               complexity of the general solution to only consider uncertain edges
               and derive an approximate measurement update. In simulations, we
               show that the proposed approach dramatically improves point-cloud-based
               estimators, especially in the presence of high noise.},
  pdf       = {Fusion13_Faion.pdf},
  month     = jul
}

@inproceedings{Fusion13_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Bearings-Only Sensor Scheduling Using Circular Statistics}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {In this paper, we introduce a novel approach for scheduled tracking
               of a moving target based on bearings-only sensors. Unlike classical
               approaches that are typically based on the extended or unscented
               Kalman filter, we rely on circular statistics to describe probability
               distributions for angular measurements more accurately. As the energy
               available to sensors is limited in many scenarios, we introduce a
               scheduling algorithm that selects a subset of two sensors to be active
               at any given time step while minimizing the uncertainty of the state
               estimate. This is done by anticipating possible future measurements.
               We evaluate the proposed method in simulations and compare it to
               an UKF-based solution. Our evaluation demonstrates the superiority
               of the presented approach, particularly when high measurement uncertainty
               makes consideration of the circular geometry necessary.},
  pdf       = {Fusion13_Gilitschenski.pdf},
  month     = jul
}

@inproceedings{Fusion13_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{PGF 42: Progressive Gaussian Filtering with a Twist}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  pdf       = {Fusion13_Hanebeck.pdf},
  abstract  = {A new Gaussian filter for estimating the state of nonlinear systems
               is derived that relies on two main ingredients: i) the progressive
               inclusion of the measurement information and ii) a tight coupling
               between a Gaussian density and its deterministic Dirac mixture approximation.
               No second Gaussian assumption for the joint density of state and
               measurement is required, so that the performance is much better than
               that of Linear Regression Kalman Filters (LRKFs), which heavily
               rely on this assumption. In addition, the new filter directly works
               with the generative system description. No Likelihood function is
               required. It can be used as a plug -in replacement for standard Gaussian
               filters such as the UKF.},
  month     = jul
}

@inproceedings{Fusion13_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Gaussian Filtering for Polynomial Systems Based on Moment Homotopy}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {This paper proposes Gaussian filters for polynomial systems with efficient
               solutions for both the prediction and the filter step. For the prediction
               step, computationally efficient closed-form solutions are derived
               for calculating the exact moments. In order to achieve a higher estimation
               quality, the filter step is solved without the usual additional assumption
               that state and measurement are jointly Gaussian distributed. As this
               significantly complicates the required moment calculation, a homotopy
               continuation method is employed that yields almost optimal results.},
  pdf       = {Fusion13_Huber.pdf},
  month     = jul
}

@inproceedings{Fusion13_Kurz-Bingham,
  author    = {Gerhard Kurz and Igor Gilitschenski and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{Recursive Estimation of Orientation Based on the Bingham Distribution}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {Directional estimation is a common problem in many tracking applications.
               Traditional filters like the Kalman filter perform poorly because
               they fail to take the periodic nature of the problem into account.
               We present a recursive filter for directional data based on the Bingham
               distribution in two dimensions. The proposed filter can be applied
               to circular filtering problems with 180 degree symmetry, i.e., rotations
               by 180 degrees cannot be distinguished. It is easily implemented
               using standard numerical techniques and suitable for real-time applications.
               The presented approach is extensible to quaternions, which allow
               tracking arbitrary three-dimensional orientations. We evaluate our
               filter in a challenging scenario and compare it to a traditional
               Kalman filtering approach.},
  pdf       = {Fusion13_Kurz-Bingham.pdf},
  annote    = {<b>Best Student Paper Award First Runner-Up</b> <a href="https://isas.iar.kit.edu/pdf/award/Fusion2013_best_student_paper_award_first_runner_up.pdf" target="_blank">Certificate (PDF)</a>},
  month     = jul
}

@inproceedings{Fusion13_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Recursive Fusion of Noisy Depth and Position Measurements for Surface Reconstruction}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  pdf       = {Fusion13_Kurz.pdf},
  abstract  = {We propose an algorithm to combine both depth and position measurements
               when estimating a continuous surface. Position measurements originate
               from a fixed point on the surface, whereas depth measurements are
               determined by the intersection of the surface with a line originating
               from the depth sensor. Through fusion of both types of measurements,
               it is possible to benefit from the advantages of different sensors.
               The surface is obtained through interpolation of control points with
               splines, which allows a compact representation of the surface. In
               order to simplify the problem of intersecting the surface with lines
               originating from the depth sensor, we propose the use of polar or
               spherical coordinates in surface parameterization. The presented
               algorithm can be applied in both 2D and 3D settings and is independent
               of the particular choice of sensors. Our method can recursively include
               new information as it is obtained by using nonlinear filtering and
               it considers uncertainties associated with the measurements.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/Fusion2013_best_paper_award.pdf" target="_blank">Certificate (PDF)</a>},
  month     = jul
}

@inproceedings{Fusion13_Noack,
  author    = {Benjamin Noack and Simon J. Julier and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{Nonlinear Federated Filtering}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {The federated Kalman filter embodies an efficient and easy-to-implement
               solution for linear distributed estimation problems. Data from independent
               sensors can be processed locally and in parallel on different nodes
               without running the risk of erroneously ignoring possible dependencies.
               The underlying idea is to counteract the common process noise issue
               by inflating the joint process noise matrix. In this paper, the same
               trick is generalized to nonlinear models and non-Gaussian process
               noise. The probability density of the joint process noise is split
               into an exponential mixture of transition densities. By this means,
               the process noise is modeled to independently affect the local system
               models. The estimation results provided by the sensor devices can
               then be fused, just as if they were indeed independent.},
  pdf       = {Fusion13_Noack.pdf},
  month     = jul
}

@inproceedings{Fusion13_Pfaff,
  author    = {Florian Pfaff and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Data Validation in the Presence of Stochastic and Set-Membership Uncertainties}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {For systems suffering from different types of uncertainties, finding
               criteria for validating measurements can be challenging. In this
               paper, we regard both stochastic Gaussian noise with full or imprecise
               knowledge about correlations and unknown but bounded errors. The
               validation problems arising in the individual and combined cases
               are illustrated to convey different perspectives on the proposed
               conditions. Furthermore, hints are provided for the algorithmic implementation
               of the validation tests. Particular focus is put on ensuring a predefined
               lower bound for the probability of correctly classifying valid data.},
  pdf       = {Fusion13_Pfaff.pdf},
  month     = jul
}

@inproceedings{Fusion13_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Advances in Hypothesizing Distributed Kalman Filtering}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {In this paper, linear distributed estimation is revisited on the basis
               of the hypothesizing distributed Kalman filter and equations for
               a flexible application of the algorithm are derived. We propose a
               new approximation for the mean-squared-error matrix and present techniques
               for automatically improving the hypothesis about the global measurement
               model. Utilizing these extensions, the precision of the filter is
               improved so that it asymptotically yields optimal results for time-invariant
               models. Pseudo-code for the implementation of the algorithm is provided
               and the lossless inclusion of out-of-sequence measurements is discussed.
               An evaluation demonstrates the effect of the new extensions and compares
               the results to state-of-the-art methods.},
  pdf       = {Fusion13_Reinhardt.pdf},
  month     = jul
}

@inproceedings{Fusion13_Sijs,
  author    = {Joris Sijs and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Event-based State Estimation with Negative Information}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {To reduce the amount of data transfer in networked systems, measurements
               are usually taken only when an event occurs rather than periodically
               in time. However, this complicates estimation problems considerably
               as it is not guaranteed that new sensor data will be sampled. Therefore,
               an existing state estimator is extended so to cope with event-based
               measurements successfully, i.e., curtail any diverging behavior in
               the estimation results. To that extent, a general formulation of
               event sampling is proposed. This formulation is used to set up a
               state estimator combining stochastic as well as set-membership measurement
               information according to a hybrid update: when an event occurs the
               estimated state is updated using the stochastic measurement received
               (positive information), while at periodic time instants no measurement
               is received (negative information) and the update is based on knowledge
               that the sensor value lies within a bounded subset of the measurement
               space. An illustrative example further shows that the developed estimator
               has an improved representation of estimation errors compared to a
               purely stochastic estimator for various event sampling strategies.},
  pdf       = {Fusion13_Sijs.pdf},
  month     = jul
}

@inproceedings{Fusion13_Steinbring,
  author    = {Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{S2KF: The Smart Sampling Kalman Filter}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems
               called Smart Sampling Kalman Filter (S²KF) is introduced. It is based
               on a new low-discrepancy Dirac Mixture approximation of Gaussian
               densities. The approximation comprises an arbitrary number of optimally
               and deterministically placed samples in the entire state space, so
               that the filter resolution can be adapted to either achieve high-quality
               results or meet computational constraints. For two samples per dimension,
               the S²KF comprises the UKF as a special case. With an increasing
               number of samples, the new filter quickly converges to the (typically
               infeasible) exact analytic LRKF. The S²KF can be seen as the ultimate
               generalization of all sample-based LRKFs such as the UKF, sigma-point
               filters, higher-order variants etc., as it homogeneously covers the
               state space with an arbitrary number of samples. It is evaluated
               by performing extended target tracking.},
  pdf       = {Fusion13_Steinbring.pdf},
  month     = jul
}

@inproceedings{Fusion13_Zea,
  author    = {Antonio Zea and Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Level-Set Random Hypersurface Models for Tracking Non-Convex Extended Objects}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {This paper presents a novel approach to track a non-convex shape approximation
               of an extended target based on noisy point measurements. For this
               purpose, a novel type of Random Hypersurface Model (RHM), called
               Level-Set RHM is introduced that models the interior of a shape with
               level-sets of an implicit function. Based on the Level-Set RHM, a
               nonlinear measurement equation can be derived that allows to employ
               a standard Gaussian state estimator for tracking an extended object
               even in scenarios with high measurement noise. In this paper, shapes
               are described using polygons and shape regularization is applied
               using ideas from active contour models.},
  pdf       = {Fusion13_Zea.pdf},
  month     = jul
}

@article{AT13_Fischer,
  author   = {J{\"{o}}rg Fischer and Marc Reinhardt and Uwe D. Hanebeck},
  title    = {{Optimal Sequence-Based Control and Estimation of Networked Linear Systems}},
  journal  = {at -- Automatisierungstechnik, Oldenbourg Verlag},
  volume   = {61},
  number   = {7},
  pages    = {467--476},
  year     = {2013},
  doi      = {10.1524/auto.2013.0039},
  url      = {https://dx.doi.org/10.1524/auto.2013.0039},
  abstract = {In this paper, a unified approach to sequence-based control and estimation
              of linear networked systems with multiple sensors is proposed. Time
              delays and data losses in the controller-actuator link are compensated
              by sending sequences of control inputs. The sequence-based design
              paradigm is further extended to the sensor-controller connections
              without increasing the load of the network. In this context, we present
              a recursive solution based on the Hypothesizing Distributed Kalman
              Filter (HKF) that is included in the overall sequence-based controller
              design.},
  pdf      = {AT13_Fischer.pdf},
  month    = jul
}

@inproceedings{ACC13_Fischer,
  author    = {J{\"{o}}rg Fischer and Achim Hekler and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Optimal Sequence-Based LQG Control over TCP-like Networks Subject to Random Transmission Delays and Packet Losses}},
  booktitle = {Proceedings of the 2013 American Control Conference (ACC 2013)},
  year      = {2013},
  address   = {Washington D.C., USA},
  pdf       = {ACC13_Fischer.pdf},
  abstract  = {This paper addresses the problem of sequence-based controller design
               for Networked Control Systems (NCS), where control inputs and measurements
               are transmitted over TCP-like network connections that are subject
               to random transmission delays and packet losses. To cope with the
               network effects, the controller not only sends the current control
               input to the actuator, but also a sequence of predicted control inputs
               at every time step. In this setup, we derive an optimal solution
               to the Linear Quadratic Gaussian (LQG) control problem and prove
               that the separation principle holds. Simulations demonstrate the
               improved performance of this optimal controller compared to other
               sequence-based approaches.},
  month     = jun
}

@inproceedings{ACC13_Gilitschenski,
  author    = {Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Efficient Deterministic Dirac Mixture Approximation}},
  booktitle = {Proceedings of the 2013 American Control Conference (ACC 2013)},
  year      = {2013},
  address   = {Washington D.C., USA},
  pdf       = {ACC13_Gilitschenski.pdf},
  abstract  = {We propose an efficient method for approximating arbitrary Gaussian
               densities by a mixture of Dirac components. This approach is based
               on the modification of the classical Cramér-von Mises distance, which
               is adapted to the multivariate scenario by using Localized Cumulative
               Distributions (LCDs) as a replacement for the cumulative distribution
               function. LCDs consider the local probabilistic influence of aprobability
               density around a given point. Our modification of the Cramér-von
               Mises distance can be approximated for certain special cases in closed-form.
               The created measure is minimized in order to compute the positions
               of the Dirac components for a standard normal distribution.},
  month     = jun
}

@inproceedings{ACC13_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Recursive Nonlinear Filtering for Angular Data Based on Circular Distributions}},
  booktitle = {Proceedings of the 2013 American Control Conference (ACC 2013)},
  year      = {2013},
  address   = {Washington D.C., USA},
  pdf       = {ACC13_Kurz.pdf},
  abstract  = {Estimation of circular quantities is a widespread problem that occurs
               in many tracking and control applications. Commonly used approaches
               such as the Kalman filter, the extended Kalman filter (EKF), and
               the unscented Kalman filter (UKF) do not take periodicity explicitly
               into account, which can result in low estimation accuracy. We present
               a filtering algorithm for angular quantities in nonlinear systems
               that is based on circular statistics. The new filter switches between
               three different representations of probability distributions on the
               circle, the wrapped normal, the von Mises, and a Dirac mixture density.
               It can be seen as a systematic generalization of the UKF to circular
               statistics. We evaluate the proposed filter in simulations and show
               its superiority to conventional approaches.},
  month     = jun
}

@article{TAES13_Baum_RHM,
  author  = {Marcus Baum and Uwe D. Hanebeck},
  title   = {{Extended Object Tracking with Random Hypersurface Models}},
  journal = {IEEE Transactions on Aerospace and Electronic Systems},
  year    = {2014},
  volume  = {50},
  pages   = {149-159},
  pdf     = {TAES13_Baum.pdf},
  doi     = {10.1109/TAES.2013.120107},
  month   = jan
}

@inproceedings{CDC12_Baum,
  author    = {Marcus Baum and Patrick Ruoff and Dominik Itte and Uwe D. Hanebeck},
  title     = {{Optimal Point Estimates for Multi-target States based on Kernel Distances}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {Almost all multi-target tracking systems have to generate point estimates
               for the targets, e.g., for displaying the tracks. The novel idea
               in this paper is to  consider point estimates for multi-target states
               that are optimal according to a  kernel distance measure. Because
               the kernel distance is a metric on point sets and ignores the target
               labels, shortcomings of  Minimum Mean Squared Error (MMSE) estimates
               for multi-target states can be avoided. We show how the calculation
               of  these point estimates can be casted as an optimization problem
               and it turns out that it corresponds to the problem of reducing the
               Probability Hypothesis Density (PHD) function   to a Dirac mixture
               density. Finally, we  discuss a generalization of the kernel distance
               called LCD distance, which does not require to choose a specific
               kernel width. The presented methods are evaluated in a  Multiple-Hypotheses
               Tracker (MHT) setting with up to ten targets.},
  pdf       = {CDC12_Baum.pdf},
  month     = dec
}

@inproceedings{CDC12_Chlebek,
  author    = {Christof Chlebek and Achim Hekler and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control Based on Progressive Density Simplification}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {Increasing demand for Nonlinear Model Predictive Control with the
               ability to handle highly noise-corrupted systems has recently given
               rise to stochastic control approaches. Besides providing high-quality
               results within a noisy environment, these approaches have one problem
               in common, namely a high computational demand and, as a consequence,
               generally a short prediction horizon. In this paper, we propose to
               reduce the computational complexity of prediction and value function
               evaluation within the control horizon by simplifying the system progressively
               down to the deterministic case. Approximation of occurring probability
               densities by a specific representation, the deterministic Dirac mixture
               density, with a decreasing resolution (i.e., approximation quality)
               leads via natural decomposition to a point estimate and thus, can
               be treated in a deterministic manner. Hence, calculation of the remaining
               time steps requires considerably less computation time.},
  pdf       = {CDC12_Chlebek.pdf},
  month     = dec
}

@inproceedings{CDC12_Hekler,
  author    = {Achim Hekler and J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {In this paper, we address the problem of controlling a system over
               an unreliable UDP-like network that is affected by time-varying delays
               and randomly occurring packet losses. A major challenge of this setup
               is that the controller just has uncertain information about the control
               inputs actually applied by the actuator. The key idea of this work
               is to model the uncertain control inputs by random variables, the
               so-called virtual control inputs, which are characterized by discrete
               probability density functions. Subject to this probabilistic description,
               a novel, easy to implement sequencebased control approach is proposed
               that extends any given state feedback controller designed without
               consideration of the network-induced disturbances. The high performance
               of the proposed controller is demonstrated by means of Monte Carlo
               simulation runs with an inverted pendulum on a cart.},
  pdf       = {CDC12_Hekler.pdf},
  month     = dec
}

@inproceedings{CDC12_Noack,
  author    = {Benjamin Noack and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Optimal Kalman Gains for Combined Stochastic and Set-Membership State Estimation}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {In state estimation theory, two directions are mainly followed in
               order to model disturbances and errors. Either uncertainties are
               modeled as stochastic quantities or they are characterized by their
               membership to a set. Both approaches have distinct advantages and
               disadvantages making each one inherently better suited to model different
               sources of estimation uncertainty. This paper is dedicated to the
               task of combining stochastic and set-membership estimation methods.
               A Kalman gain is derived that minimizes the mean squared error in
               the presence of both stochastic and additional unknown but bounded
               uncertainties, which are represented by Gaussian random variables
               and ellipsoidal sets, respectively. As a result, a generalization
               of the well-known Kalman filtering scheme is attained that reduces
               to the standard Kalman filter in the absence of set-membership uncertainty
               and that otherwise becomes the intersection of sets in case of vanishing
               stochastic uncertainty. The proposed concept also allows to prioritize
               either the minimization of the stochastic uncertainty or the minimization
               of the set-membership uncertainty.},
  pdf       = {CDC12_Noack.pdf},
  month     = dec
}

@inproceedings{CDC12_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Decentralized Control Based on Globally Optimal Estimation}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {A new method for globally optimal estimation in decentralized sensor-networks
               is applied to the decentralized control problem. The resulting approach
               is proven to be optimal when the nodes have access to all information
               in the network. More precisely, we utilize an algorithm for optimal
               distributed estimation in order to obtain local estimates whose combination
               yields the globally optimal estimate. When the interconnectivity
               is high, the local estimates are almost optimal, which motivates
               the application of the principle of separation. Thus, we optimize
               the controller and finally obtain a flexible algorithm, whose quality
               is evaluated in different scenarios. In applications where the strong
               requirements on a perfect communication cannot be guaranteed, we
               derive quality bounds by help of a detailed evaluation of the algorithm.
               When information is regularly exchanged, it is demonstrated that
               the algorithm performs almost optimally and therefore, offers system
               designers a flexible and easy to implement approach. The field of
               applications lies within the area of strongly networked systems,
               in particular, when communication disturbances cannot be foreseen
               or when the network structure is too complicated to apply optimized
               regulators.},
  pdf       = {CDC12_Reinhardt.pdf},
  month     = dec
}

@inproceedings{CURAC12_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Image Stabilization with Model-Based Tracking for Beating Heart Surgery}},
  booktitle = {11. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC12)},
  year      = {2012},
  address   = {D{\"{u}}sseldorf, Germany},
  month     = nov,
  abstract  = {Performing surgery on the beating heart has significant advantages
               compared to cardiopulmonary bypass. However, when performed directly,
               it is very demanding for the surgeon. As an alternative, using a
               teleoperated robot for compensating the heart motion has been proposed.
               As an addition, this paper describes how stabilized images are obtained
               to create the illusion of operating on a stationary heart. For that
               purpose, the heart motion is tracked with a stochastic physical model.
               Based on correspondences obtained by motion tracking, image stabilization
               is considered as a scattered data interpolation problem. The proposed
               algorithms are evaluated on a heart phantom and in in-vivo experiments
               on a porcine heart, which show that there is very little residual
               motion in the stabilized images.},
  pdf       = {CURAC12_Kurz.pdf}
}

@article{TAES12_Baum,
  author   = {Marcus Baum and Uwe D. Hanebeck},
  title    = {{Extended Object Tracking Based on Set-Theoretic and Stochastic Fusion}},
  journal  = {IEEE Transactions on Aerospace and Electronic Systems},
  year     = {2012},
  volume   = {48},
  pages    = {3103--3115},
  number   = {4},
  month    = oct,
  abstract = {A novel approach for extended object tracking is presented. In contrast
              to existing approaches, no statistical assumptions about the location
              of the measurement sources on the extended target object are made.
              As a consequence, a combined set-theoretic and stochastic estimator
              is obtained that is robust to systematic errors in the target model.
              The benefits of the new approach is demonstrated by means of simulations.},
  doi      = {10.1109/TAES.2012.6324680},
  url      = {https://dx.doi.org/10.1109/TAES.2012.6324680},
  issn     = {0018-9251},
  pdf      = {TAES12_Baum.pdf}
}

@inproceedings{IROS12_Faion,
  author    = {Florian Faion and Simon Friedberger and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Intelligent Sensor-Scheduling for Multi-Kinect-Tracking}},
  booktitle = {Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012)},
  year      = {2012},
  address   = {Vilamoura, Algarve, Portugal},
  month     = oct,
  abstract  = {This paper describes a method to intelligently schedule a network
               of multiple RGBD sensors in a Bayesian object tracking scenario,
               with special focus on Microsoft Kinect devices. These setups have
               issues such as the large amount of raw data generated by the sensors
               and interference caused by overlapping fields of view. The proposed
               algorithm addresses these issues by selecting and exclusively activating
               the sensor that yields the best measurement, as defined by a novel
               stochastic model that also considers hardware constraints and intrinsic
               parameters. In addition, as existing solutions to toggle the sensors
               were found to be insufficient, the development of a hardware module,
               especially designed for quick toggling and synchronization with the
               depth stream, is also discussed. The algorithm then is evaluated
               within the scope of a multi-Kinect object tracking scenario and compared
               to other scheduling strategies.},
  pdf       = {IROS12_Faion.pdf}
}

@inproceedings{MFI12_Baum,
  author    = {Marcus Baum and Florian Faion and Uwe D. Hanebeck},
  title     = {{Tracking Ground Moving Extended Objects using RGBD Data}},
  booktitle = {Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)},
  year      = {2012},
  address   = {Hamburg, Germany},
  month     = sep,
  abstract  = {This paper is about an experimental set-up for tracking a ground moving
               mobile object from a bird's eye view. In this experiment, an RGB
               and depth camera is used for detecting moving points. The detected
               points serve as input for a probabilistic extended object tracking
               algorithm that simultaneously estimates the kinematic parameters
               and the shape parameters of the object. By this means, it is easy
               to discriminate moving objects from the background and the probabilistic
               tracking algorithm ensures a robust and smooth shape estimate. We
               provide an experimental evaluation of a recent Bayesian extended
               object tracking algorithm based on a so-called Random Hypersurface
               Model and give a comparison with active contour models.},
  annote    = {<b>Nominee Best Student Paper Award</b>},
  pdf       = {MFI12_Baum.pdf}
}

@inproceedings{MFI12_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{The Hypothesizing Distributed Kalman Filter}},
  booktitle = {Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)},
  year      = {2012},
  address   = {Hamburg, Germany},
  month     = sep,
  abstract  = {This paper deals with distributed information processing in sensor
               networks. We propose the Hypothesizing Distributed Kalman Filter
               that incorporates an assumption of the global measurement model into
               the distributed estimation process. The procedure is based on the
               Distributed Kalman Filter and inherits its optimality when the assumption
               about the global measurement uncertainty is met. Recursive formulas
               for local processing as well as for fusion are derived. We show that
               the proposed algorithm yields the same results, no matter whether
               the measurements are processed locally or globally, even when the
               process noise is not negligible. For further processing of the estimates,
               a consistent bound for the error covariance matrix is derived. All
               derivations and explanations are illustrated by means of a new classification
               scheme for estimation processes.},
  annote    = {<b>Nominee Best Student Paper Award</b>},
  pdf       = {MFI12_Reinhardt.pdf}
}



@article{SYSAES_Baum,
  author   = {Marcus Baum},
  title    = {{Student Research Highlight: Simultaneous Tracking and Shape Estimation of Extended Targets}},
  journal  = {IEEE Aerospace and Electronic Systems Magazine},
  year     = {2012},
  volume   = {27},
  pages    = {42--44},
  number   = {7},
  month    = jul,
  abstract = {Target tracking algorithms are usually based on the assumption that
              the target extent is small compared to the measurement noise; hence,
              the target is modeled as a mathematical point. However, if the target
              extent is rather large, the target may cause multiple sensor measurements
              from different spatially distributed reflection centers. In this
              case, the modeling of the target extent is essential. In particular,
              the author looks at the random hypersurface model and its role in
              tracking; the tracking method is evaluated using a Microsoft(R) Kinect(TM)
              sensor as an example.},
  doi      = {10.1109/MAES.2012.6328840},
  issn     = {0885-8985},
  url      = {https://dx.doi.org/10.1109/MAES.2012.6328840}
}




@article{TAC12_Deisenroth,
  author   = {Marc Peter Deisenroth and Ryan Darby Turner and Marco F. Huber and Uwe D. Hanebeck and Carl Edward Rasmussen},
  title    = {{Robust Filtering and Smoothing with Gaussian Processes}},
  journal  = {IEEE Transactions on Automatic Control},
  year     = {2012},
  volume   = {57},
  pages    = {1865--1871},
  number   = {7},
  month    = jul,
  abstract = {We propose a principled algorithm for robust Bayesian filtering and
              smoothing in nonlinear stochastic dynamic systems when both the transition
              function and the measurement function are described by non-parametric
              Gaussian process (GP) models. GPs are gaining increasing importance
              in signal processing, machine learning, robotics, and control for
              representing unknown system functions by posterior probability distributions.
              This modern way of system identification is more robust than finding
              point estimates of a parametric function representation. Our principled
              filtering/smoothing approach for GP dynamic systems is based on analytic
              moment matching in the context of the forward-backward algorithm.
              Our numerical evaluations demonstrate the robustness of the proposed
              approach in situations where other state-of-the-art Gaussian filters
              and smoothers can fail.},
  doi      = {10.1109/TAC.2011.2179426},
  issn     = {0018-9286},
  keywords = {GP dynamic systems;analytic moment matching;control systems;forward-backward
              algorithm;machine learning;measurement function;nonlinear stochastic
              dynamic systems;nonparametric Gaussian process;parametric function
              representation;point estimation;posterior probability distributions;robotics;robust
              Bayesian filtering;robust Bayesian smoothing;signal processing;system
              identification;transition function;unknown system function representation;Bayes
              methods;Gaussian processes;identification;nonlinear dynamical systems;nonparametric
              statistics;smoothing methods;statistical distributions;},
  pdf      = {TAC12_Deisenroth.pdf},
  url      = {https://dx.doi.org/10.1109/TAC.2011.2179426}
}

@inproceedings{Fusion12_Baum,
  author    = {Marcus Baum and Florian Faion and Uwe D. Hanebeck},
  title     = {{Modeling the Target Extent with Multiplicative Noise}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Extended target tracking deals with simultaneously tracking the shape
               and the kinematic parameters of a target. In this work, we formulate
               the extended target tracking problem as a state estimation problem
               with both multiplicative and additive measurement noise. In case
               of extended targets with known orientation, we show that the best
               linear estimator is not consistent and, hence, is unsuitable for
               this problem. In order to overcome this issue, we propose a quadratic
               estimator for a recursive closed-form measurement update. Simulations
               demonstrate the performance of the estimator.},
  pdf       = {Fusion12_Baum-MultNoise.pdf}
}

@inproceedings{Fusion12_BaumWillett,
  author    = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title     = {{Calculating Some Exact MMOSPA Estimates for Particle Distributions}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In this work, we present some exact algorithms for calculating the
               minimum mean optimal sub-pattern assignment (MMOSPA) estimate for
               probability densities that are represented with particles. First,
               a polynomial-time algorithm for two targets is derived by reducing
               the problem to the enumeration of the cells of a hyperplane arrangement.
               Second, we present a linear-time algorithm for an arbitrary number
               of scalar target states, which is based on the insight that the MMOSPA
               estimate coincides with the mean of the order statistics.},
  pdf       = {Fusion12_Baum-MMOSPA.pdf}
}

@inproceedings{Fusion12_BenavoliNoack,
  author    = {Alessio Benavoli and Benjamin Noack},
  title     = {{Pushing Kalman's Idea to the Extremes}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {The paper focuses on the fundamental idea of Kalman's seminal paper:
               how to solve the filtering problem from the only knowledge of the
               first two moments of the noise terms. In this paper, by exploiting
               set of distributions based filtering, we solve this problem without
               introducing additional assumptions on the distributions of the noise
               terms (e.g., Gaussianity) or on the final form of the estimator (e.g.,
               linear estimator). Given the moments (e.g., mean and variance) of
               random variable X, it is possible to define the set of all distributions
               that are compatible with the moments information. This set of distributions
               can be equivalently characterized by its extreme distributions which
               is a family of mixtures of Dirac's deltas. The lower and upper expectation
               of any function g of X are obtained in correspondence of these extremes
               and can be computed by solving a linear programming problem. The
               filtering problem can then be solved by running iteratively this
               linear programming problem.},
  pdf       = {Fusion12_BenavoliNoack.pdf}
}

@inproceedings{Fusion12_Faion-CylinderTracking,
  author    = {Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Depth sensors such as the Microsoft Kinect\texttrademark depth sensor
               provide three dimensional point clouds of an observed scene. In this
               paper, we employ Random Hypersurface Models (RHMs), which is a modeling
               technique for extended object tracking, to point cloud fusion in
               order to track a shape approximation of an underlying object. We
               present a novel variant of RHMs to model shapes in 3D space. Based
               on this novel model, we develop a specialized algorithm to track
               persons by approximating their shapes as cylinders. For evaluation,
               we utilize a Kinect network and simulations based on a stochastic
               sensor model.},
  pdf       = {Fusion12_Faion-CylinderTracking.pdf}
}

@inproceedings{Fusion12_Faion-RecursiveCalibration,
  author    = {Florian Faion and Patrick Ruoff and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Recursive Bayesian Calibration of Depth Sensors with Non-Overlapping Views}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In this paper we present a recursive Bayesian method to calibrate
               rigidly linked depth sensors with non-overlapping fields of view.
               The extrinsic parameters of this setup are obtained by rotating and
               translating both cameras, estimating the local transformations using
               point feature correspondences, and finally using these values to
               recursively find a solution to the matrix equation $\mat{A_k}\mat{X}=\mat{X}\mat{B_k}$.
               The algorithm is based on a Bayesian estimator, which allows the
               consideration of camera-specific measurement noise and permits the
               system to adapt naturally to changes in the extrinsic parameters.
               The derived equations were carefully chosen to be free from singularities.
               This paper also includes a thorough evaluation based on synthetic
               and real data to show the effectiveness of the algorithm.},
  pdf       = {Fusion12_Faion.pdf}
}

@inproceedings{Fusion12_Fischer,
  author    = {J{\"{o}}rg Fischer and Achim Hekler and Uwe D. Hanebeck},
  title     = {{State Estimation in Networked Control Systems}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {We consider the problem of state estimation in a Networked Control
               System, where measurements and control inputs are transmitted via
               a communication network. The network is subject to time-varying delays
               and stochastic data losses and does not provide acknowledgments of
               successfully transmitted data packets. A challenge that arises in
               this configuration is that the estimator has only uncertain information
               about the actually applied control inputs. In this paper, we derive
               a multiple-model based estimator that uses the state measurements
               to estimate the applied control inputs so that the overall state
               estimation is improved. The efficiency of the proposed approach is
               demonstrated by means of Monte-Carlo-Simulation runs with an inverted
               pendulum on a cart.},
  pdf       = {Fusion12_Fischer.pdf}
}

@inproceedings{Fusion12_Gilitschenski,
  author    = {Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{A Robust Computational Test for Overlap of Two Arbitrary-dimensional Ellipsoids in Fault-Detection of Kalman Filters}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {On-line fault-detection in uncertain measurement and estimation systems
               is of particular interest in many applications. In certain systems
               based on the Kalman filter, this test can be performed by checking
               whether hyperellipsoids overlap. This test can be applied to detecting
               failure in the system itself or in the sensors used to determine
               the system state. To facilitate the practical application of such
               tests, we describe a simple condition for overlap of two ellipsoids
               and propose an efficient algorithmic implementation for testing this
               condition. There are applications in many other areas, such as collision
               avoidance or computer graphics. Our proposal makes use of Leverriere's
               algorithm and Sturm's theorem, a result of algebraic geometry. Thus,
               no approximative methods, such as root finding or minimization are
               needed. Furthermore, the complexity of the algorithm is fixed for
               a fixed problem dimension.},
  pdf       = {Fusion12_Gilitschenski.pdf}
}

@inproceedings{Fusion12_Hanebeck,
  author    = {Uwe D. Hanebeck and Jannik Steinbring},
  title     = {{Progressive Gaussian Filtering Based on Dirac Mixture Approximations}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In this paper, we propose a progressive Gaussian filter, where the
               measurement information is continuously included into the given prior
               estimate (although we perform observations at discrete time steps).
               The key idea is to derive a system of ordinary first-order differential
               equations (ODE) that is used for continuously tracking the true non-Gaussian
               posterior by its best matching Gaussian approximation. Calculating
               the required moments of the true posterior is performed based on
               corresponding Dirac Mixture approximations. The performance of the
               new filter is evaluated in comparison with state-of-the-art filters
               by means of a canonical benchmark example, the discrete-time cubic
               sensor problem.},
  pdf       = {Fusion12_Hanebeck.pdf}
}

@inproceedings{Fusion12_Hekler,
  author    = {Achim Hekler and J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{Control over Unreliable Networks Based on Control Input Densities}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Time delays and data losses arising from an unreliable communication
               between the components of a control loop decrease the quality of
               control and thus, have to be incorporated explicitly in the control
               decision. In this paper, a novel concept, the so-called virtual control
               inputs, is presented, which extends the well-established control
               technique of sending sequences of future inputs by incorporating
               also the potential effects of previously transmitted sequences on
               the future system behavior. The key idea of this paper is to model
               the unknown future inputs as random variables characterized by probability
               density functions over the finite set of potential future inputs.
               Subject to this probabilistic description of the future inputs, the
               controller determines the optimal open-loop sequence over a finite
               horizon. The high capacity of the proposed approach is demonstrated
               by simulations, in which a sensor manager schedules sensors for tracking
               a mobile object.},
  pdf       = {Fusion12_Hekler.pdf}
}

@inproceedings{Fusion12_Noack,
  author    = {Benjamin Noack and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Combined Stochastic and Set-Membership Information Filtering in Multisensor Systems}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In state estimation theory, stochastic and set-membership approaches
               are generally considered separately from each other. Both concepts
               have distinct advantages and disadvantages making each one inherently
               better suited to model different sources of estimation uncertainty.
               In order to better utilize the potentials of both concepts, the core
               element of this paper is a Kalman filtering scheme that allows for
               a simultaneous treatment of stochastic and set-membership uncertainties.
               An uncertain quantity is herein modeled by a set of Gaussian densities.
               Since many modern applications operate in networked systems that
               may consist of a multitude of local processing units and sensor nodes,
               estimates have to be computed in a distributed manner and measurements
               may arrive at high frequency. An algebraic reformulation of the Kalman
               filter, the information filter, significantly eases the implementation
               of such distributed fusion architectures. This paper explicates how
               stochastic and set-membership uncertainties can simultaneously be
               treated within this information form and compared to the Kalman filter,
               it becomes apparent that the quality of some required approximations
               is enhanced.},
  pdf       = {Fusion12_Noack.pdf}
}

@inproceedings{Fusion12_Packi,
  author    = {Ferdinand Packi and Uwe D. Hanebeck},
  title     = {{Robust NLOS Discrimination for Range-Based Acoustic Pose Tracking}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Indoor localization is a field in research with many competing technologies
               using different kinds of media. A common challenge faced by most
               systems is dealing with Non-Line-of-Sight (NLOS) conditions. We are
               addressing this issue with focus on sound in the frequency range
               above 20 kHz, as we encountered severe occurrence of outliers due
               to multipath propagation, by reflections, and from occlusion. The
               proper discrimination of erroneous signals is of special concern
               during initialization time of the tracking system. During run time,
               the computationally demanding process can be spared, if motion is
               modelled and stochastic filtering techniques are applied. This paper
               depicts solutions for both cases, and demonstrates that a combined
               use of static and dynamic localization methods delivers increased
               robustness at an affordable computational cost.},
  pdf       = {Fusion12_Packi.pdf}
}

@inproceedings{Fusion12_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{On Optimal Distributed Kalman Filtering in Non-ideal Situations}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {The distributed processing of measurements and the subsequent data
               fusion is called Track-to-Track fusion. Although a solution for the
               Track-to-Track fusion that is equivalent to a central processing
               scheme has been proposed, this algorithm suffers from strict requirements
               regarding the local availability of knowledge about utilized models
               of the remote nodes. By means of simple examples, we investigate
               the effects of incorrectly assumed models and trace the errors back
               to a bias, which we derive in closed form. We propose an extension
               to the exact Track-to-Track fusion algorithm that corrects the bias
               after arbitrarily many time steps. This new approach yields optimal
               results when the assumptions about the measurement models are correct
               and otherwise still provides the exact value for the mean-squared-error
               matrix. The performance of this algorithm is demonstrated and applications
               are presented that, e.g.,~allow the employment of nonlinear filter
               methods.},
  pdf       = {Fusion12_Reinhardt.pdf}
}

@inproceedings{Fusion12_Reinhardt-FastCI,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Closed-form Optimization of Covariance Intersection for Low-dimensional Matrices}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {The fusion under unknown correlations is an important technique in
               sensor-network information processing as the cross-correlations between
               different estimates remain often unknown to the nodes. Covariance
               intersection is a wide-spread and efficient algorithm to fuse estimates
               under such uncertain conditions. Although different optimization
               criteria have been developed, the trace or determinant minimization
               of the fused covariance matrix seems to be most meaningful. However,
               this minimization requires numeric solutions of a convex optimization
               problem. We derive an algorithm to reduce this nonlinear optimization
               to the well-known polynomial root-finding problem. This allows us
               to present closed-form solutions for the determinant criterion when
               the dimension of the occurring covariance matrices is at most~4 and
               for the trace criterion when the dimension of the covariance matrices
               is at most~3. We demonstrate the effectiveness of the approach by
               means of a speed evaluation.},
  pdf       = {Fusion12_Reinhardt-FastCI.pdf}
}

@inproceedings{ACC12_Hekler,
  author    = {Achim Hekler and Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities}},
  booktitle = {Proceedings of the 2012 American Control Conference (ACC 2012)},
  year      = {2012},
  address   = {Montr\'{e}al, Canada},
  month     = jun,
  abstract  = {The main problem of stochastic nonlinear model predictive control
               (SNMPC) is that the equations for state prediction and calculation
               of the expected reward are in general not solvable in closed form.
               A popular approach is to approximate the occurring continuous probability
               density functions by a discrete density representation, which allows
               an analytical solution of the SNMPC equations. In this paper, we
               propose to draw the samples not randomly as in Monte Carlo based
               methods, but systematically by minimizing a distance measure. In
               doing so, fewer components are generally required to represent the
               underlying probability density while achieving the same approximation
               quality. Especially if the evaluation of the expected reward is computationally
               expensive, this property affects the complexity of computation significantly.
               By means of a path planning problem, we have substantiated this statement
               with several simulation runs.},
  pdf       = {ACC12_Hekler.pdf}
}

@inproceedings{ACC12_Lyons,
  author    = {Daniel Lyons and Jan-Peter Calliess and Uwe D. Hanebeck},
  title     = {{Chance Constrained Model Predictive Control for Multi-Agent Systems with Coupling Constraints}},
  booktitle = {Proceedings of the 2012 American Control Conference (ACC 2012)},
  year      = {2012},
  address   = {Montr\'{e}al, Canada},
  month     = jun,
  abstract  = {We consider stochastic model predictive control of a multi-agent systems
               with constraints on the probabilities of inter-agent collisions.
               First, we propose a method based on sample average approximation
               of the collision probabilities to make the stochastic control problem
               computationally tractable. Its approximation error vanishes for fixed
               control inputs as the number of samples goes to infinity. However,
               empirical results indicate that the complexity of the resulting optimization
               problem can be too high to be solved under under real-time requirements.
               To alleviate the computational burden we propose a second approach
               that uses probabilistic bounds to determine regions of increased
               probability of presence for each agent and introduce constraints
               for the control problem prohibiting overlap of these regions. We
               prove that the resulting problem is conservative for the original
               problem, i.e., every control strategy that is feasible under our
               new constraints will automatically be feasible for the true original
               problem. Furthermore, we present simulations demonstrating better
               run-time performance of our second approach compared to the sample
               average approximation. Finally, we empirically show that our approach
               is better suited than robust control approaches for situations in
               which the systems under control are affected by stochastic disturbances.},
  pdf       = {ACC12_Lyons.pdf}
}

@inproceedings{SPIE12_FischerBaum,
  author    = {Yvonne Fischer and Marcus Baum and Fabian Flohr and Uwe D. Hanebeck and J{\"{u}}rgen Beyerer},
  booktitle = {Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE)},
  title     = {{Evaluation of Tracking Methods for Maritime Surveillance }},
  year      = {2012},
  address   = {Baltimore, Maryland, USA},
  month     = apr,
  abstract  = {In this article we present an evaluation of different target tracking
               methods based on various simulated scenarios in the maritime domain.
               We implemented well known algorithms (JIPDA, Linear Multi Target
               PDA, Linear Joint PDA, Monte Carlo Markov Chain Data Association)
               and integrated them into a data fusion architecture. The algorithms
               have been compared based on extensions of the Optimal Subpattern
               Assignment metric. Also further performance measures are used to
               get a single score for each algorithm. As no single algorithm is
               equally well fitted to all tested scenarios, our results show which
               algorithms fits best for specific scenarios.},
  doi       = {10.1117/12.919234},
  url       = {https://dx.doi.org/10.1117/12.919234}
}


@inproceedings{TRB12_Kretz,
  author    = {Tobias Kretz and Antonia P{\'{e}}rez Arias and Simon Friedberger and Uwe D. Hanebeck},
  title     = {{Using Extended Range Telepresence to Collect Data of Pedestrian Dynamics}},
  booktitle = {{Proceedings of the Transportation Research Board 91st Annual Meeting (TRB 2012)}},
  year      = {2012},
  address   = {Washington D.C., USA},
  month     = jan,
  abstract  = {In this article a new way to collect data of pedestrian dynamics is
               introduced. A virtual reality system consisting of an extended range
               telepresence system and a microscopic pedestrian simulation is used
               to simplify data collection. The extended range telepresence system
               allows a user to move through a virtual environment by natural walking
               instead of by using conventional input devices, like a joystick.
               The telepresence system is connected to a pedestrian simulation which
               produces real time 3D animated output which is presented to the user
               with a head-mounted display (HMD) capable of showing 3D imagery.
               The simulated pedestrians react to the user of the telepresence as
               if it were another simulated pedestrian. With this system data about
               pedestrian dynamics can be collected in experiments in which not
               all participants need to be real people, but some - ideally all except
               for one - can be simulated. This allows the general collection of
               data about pedestrian dynamics but also to calibrate model specific
               parameters. In this paper three experiments are introduced. However,
               the focus of the contribution is to give an idea and an overview
               of the combined telepresence-simulation system as data collection
               tool.},
  pdf       = {TRB12_Kretz.pdf}
}

@inproceedings{CDC11_Baum,
  author    = {Marcus Baum and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Random Hypersurface Mixture Models for Tracking Multiple Extended Objects}},
  booktitle = {{Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)}},
  year      = {2011},
  address   = {Orlando, Florida, USA},
  month     = dec,
  abstract  = {This paper presents a novel method for tracking multiple extended
               objects. The shape of a single extended object is modeled with a
               recently developed approach called Random Hypersurface Model (RHM)
               that assumes a varying number of measurement sources to lie on scaled
               versions of the shape boundaries. This approach is extended by introducing
               a so-called Mixture Random Hypersurface Model (Mixture RHM), which
               allows for modeling multiple extended targets. Based on this model,
               a Gaussian-assumed Bayesian tracking method that provides the means
               to track and estimate shapes of multiple extended targets is derived.
               Simulations demonstrate the performance of the new approach.},
  pdf       = {CDC11_Baum.pdf}
}

@inproceedings{CDC11_Hekler,
  author    = {Achim Hekler and Martin Kiefel and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets}},
  booktitle = {{Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)}},
  year      = {2011},
  address   = {Orlando, Florida, USA},
  month     = dec,
  abstract  = {In model predictive control, a high quality of control can only be
               achieved, if the model of the system reflects the real-world process
               as precisely as possible. Therefore, the controller should be capable
               of both handling a nonlinear system description and systematically
               incorporating uncertainties affecting the system. Since stochastic
               nonlinear model predictive control (SNMPC) problems in general cannot
               be solved in closed form, either the system model or the occurring
               densities have to be approximated. In this paper, we present an SNMPC
               framework, which approximates the densities and the reward function
               by their wavelet expansions. Due to the few requirements on the shape
               and family of the densities or reward function, the presented technique
               can be applied to a large class of SNMPC problems. For accelerating
               the optimization, we additionally present a novel thresholding technique,
               the so-called dynamic thresholding, which neglects coefficients that
               are insignificant, while at the same time guaranteeing that the optimal
               control input is still chosen. The capabilities of the proposed approach
               are demonstrated by simulations with a path planning scenario.},
  pdf       = {CDC11_Hekler.pdf}
}

@inproceedings{ICCV11_Kretz,
  author    = {Tobias Kretz and Stefan Hengst and Vidal Roca and Antonia P{\'{e}}rez Arias and Simon Friedberger and Uwe D. Hanebeck},
  title     = {{Calibrating Dynamic Pedestrian Route Choice with an Extended Range Telepresence System}},
  booktitle = {{Proceedings of the first IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds in conjunction with the 13th International Conference on Computer Vision (ICCV 2011)}},
  year      = {2011},
  address   = {Barcelona, Spain},
  month     = nov,
  abstract  = {In this contribution we present the results of a pilot study in which
               an Extended Range Telepresence System is used to calibrate parameters
               of a pedestrian model for simulation. The parameters control a model
               element that is intended to make simulated agents walk in the direction
               of the esti- mated smallest remaining travel time. We use this to,
               first, show that that an Extended Range Telepresence System can serve
               for such a task in general and second to actually find simulation
               parameters that yield realistic results.},
  pdf       = {ICCV11_Kretz.pdf}
}

@inproceedings{Humanoids11_Rybok,
  author    = {Lukas Rybok and Simon Friedberger and Uwe D. Hanebeck and Rainer Stiefelhagen},
  title     = {{The KIT Robo-Kitchen Data set for the Evaluation of View-based Activity Recognition Systems}},
  booktitle = {Proceedings of the 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011)},
  year      = {2011},
  address   = {Bled, Slovenia},
  month     = oct,
  abstract  = {Human action and activity recognition from videos has attracted an
               increasing number of researchers in recent years. However, most of
               the works aim at multimedia retrieval and surveillance applications,
               but rarely at humanoid household robots, even though the robotic
               perception of human activities would allow a more natural human-robot
               interaction (HRI). To encourage future studies in this domain, we
               present in this work a novel data set specifically designed for the
               application in HRI scenarios. This Robo-kitchen data set consists
               of 14 typical kitchen activities recorded in two different stereo-camera
               setups, and each performed by 17 subjects. To establish a baseline
               for future work, we extend a state-of-the-art action recognition
               method to be applicable on the activity classification problem and
               evaluate it on the Robo-kitchen data set showing promising results.},
  pdf       = {Humanoids11_Rybok.pdf}
}

@inproceedings{SDF11_Huber,
  author    = {Marco Huber and Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Superficial Gaussian Mixture Reduction}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2011)}},
  year      = {2011},
  address   = {Berlin, Germany},
  month     = oct,
  abstract  = {Many information fusion tasks involve the processing of Gaussian mixtures
               with simple underlying shape, but many components. This paper addresses
               the problem of reducing the number of components, allowing for faster
               density processing. The proposed approach is based on identifying
               components irrelevant for the overall density's shape by means of
               the curvature of the density's surface. The key idea is to minimize
               an upper bound of the curvature while maintaining a low global reduction
               error by optimizing the weights of the original Gaussian mixture
               only. The mixture is reduced by assigning zero weights to reducible
               components. The main advantages are an alleviation of the model selection
               problem, as the number of components is chosen by the algorithm automatically,
               the derivation of simple curvature-based penalty terms, and an easy,
               efficient implementation. A series of experiments shows the approach
               to provide a good trade-off between quality and sparsity.},
  pdf       = {SDF11_Huber.pdf}
}

@inproceedings{IROS11_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Fitting Conics to Noisy Data Using Stochastic Linearization}},
  booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = sep,
  abstract  = {Fitting conic sections, e.g., ellipses or circles, to noisy data points
               is a fundamental sensor data processing problem, which frequently
               arises in robotics. In this paper, we introduce a new procedure for
               deriving a recursive Gaussian state estimator for fitting conics
               to data corrupted by additive Gaussian noise. For this purpose, the
               original exact implicit measurement equation is reformulated with
               the help of suitable approximations as an explicit measurement equation
               corrupted by multiplicative noise. Based on stochastic linearization,
               an efficient Gaussian state estimator is derived for the explicit
               measurement equation. The performance of the new approach is evaluated
               by means of a typical ellipse fitting scenario.},
  pdf       = {IROS11_Baum.pdf}
}

@inproceedings{IROS11_Gehrig,
  author    = {Dirk Gehrig and Peter Krauthausen and Lukas Rybok and Hildegard K{\"{u}}hne and Tanja Schultz and Uwe D. Hanebeck and Rainer Stiefelhagen},
  title     = {{Combined Intention, Activity, and Motion Recognition for a Humanoid Household Robot}},
  booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = sep,
  abstract  = {In this paper, a multi-level approach to intention, activity, and
               motion recognition for a humanoid robot is proposed. Our system processes
               images from a monocular camera and combines this information with
               domain knowledge. The recognition works on-line and in real-time,
               it is independent of the test person, but limited to predefined view-points.
               Main contributions of this paper are the extensible, multi-level
               modeling of the robot's vision system, the efficient activity and
               motion recognition, and the asynchronous information fusion based
               on generic processing of mid-level recognition results. The complementarity
               of the activity and motion recognition renders the approach robust
               against misclassifications. Experimental results on a real-world
               data set of complex kitchen tasks, e.g., Prepare Cereals or Lay Table,
               prove the performance and robustness of the multi-level recognition
               approach.},
  pdf       = {IROS11_Gehrig.pdf}
}

@inproceedings{IROS11_Perez,
  author    = {Antonia P{\'{e}}rez Arias and Uwe D. Hanebeck},
  title     = {{Motion Control of a Semi-mobile Haptic Interface for Extended Range Telepresence}},
  booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = sep,
  abstract  = {This paper presents the control concept of a semimobile haptic interface
               for extended range telepresence that enables the user to explore
               spatially unrestricted target environments even from a small user
               environment. The semi-mobile haptic interface consists of a haptic
               manipulator mounted on a large grounded Cartesian robot, the prepositioning
               unit. The prepositioning unit is controlled in such a way that the
               haptic manipulator is kept off its workspace limits. At the same
               time, the control algorithm allows the optimal utilization of the
               available space in the user environment and guarantees the safety
               of the user. The proposed control method is based on the position
               and velocity of the end-effector and also takes the position of the
               user into account. Moreover, it is robust against noisy measurements
               of the user position or outliers due, for example, to occlusions
               in the tracking system. Experimental results show the suitability
               of the proposed control to provide haptic interaction in extended
               range telepresence.},
  pdf       = {IROS11_Perez.pdf}
}

@inproceedings{TGF11_Kretz,
  author    = {Tobias Kretz and Stefan Hengst and Antonia P{\'{e}}rez Arias and Simon Friedberger and Uwe D. Hanebeck},
  title     = {{Using Extended Range Telepresence to Investigate Route Choice Behavior}},
  booktitle = {{Proceedings of the Traffic and Granular Flow Conference 2011 (TGF 2011)}},
  year      = {2011},
  address   = {Moscow, Russia},
  month     = sep,
  abstract  = {A combination of a telepresence system and a microscopic traffic simulator
               is introduced. It is evaluated using a hotel evacuation scenario.
               Four different kinds of supporting information are compared, standard
               exit signs, floor plans with indicated exit routes, guiding lines
               on the floor and simulated agents leading the way. The results indicate
               that guiding lines are the most efficient way to support an evacuation
               but the natural behavior of following others comes very close. On
               another level the results are consistent with previously performed
               real and virtual experiments and validate the use of a telepresence
               system in evacuation studies. It is shown that using a microscopic
               traffic simulator extends the possibilities for evaluation, e.g.
               by adding simulated humans to the environment.},
  pdf       = {TGF11_Kretz.pdf}
}

@inproceedings{IFAC11_Huber,
  author    = {Marco F. Huber and Frederik Beutler and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 18th IFAC World Congress (IFAC 2011)},
  title     = {{(Semi-)Analytic Gaussian Mixture Filter}},
  year      = {2011},
  address   = {Milan, Italy},
  month     = aug,
  abstract  = {In nonlinear filtering, special types of Gaussian mixture filters
               are a straightforward extension of Gaussian filters, where linearizing
               the system model is performed individually for each Gaussian component.
               In this paper, two novel types of linearization are combined with
               Gaussian mixture filters. The first linearization is called analytic
               stochastic linearization, where the linearization is performed analytically
               and exactly, i.e., without Taylor-series expansion or approximate
               sample-based density representation. In cases where a full analytical
               linearization is not possible, the second approach decomposes the
               nonlinear system into a set of nonlinear subsystems that are conditionally
               integrable in closed form. These approaches are more accurate than
               fully applying classical linearization.},
  pdf       = {IFAC11_Huber.pdf}
}

@inproceedings{IFAC11_Noack,
  author    = {Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation}},
  booktitle = {Proceedings of the 18th IFAC World Congress (IFAC 2011)},
  year      = {2011},
  address   = {Milan, Italy},
  month     = aug,
  abstract  = {Especially in the field of sensor networks and multi-robot systems,
               fully decentralized estimation techniques are of particular interest.
               As the required elimination of the complex dependencies between estimates
               generally yields inconsistent results, several approaches, e.g.,
               covariance intersection, maintain consistency by providing conservative
               estimates. Unfortunately, these estimates are often too conservative
               and therefore, much less informative than a corresponding centralized
               approach. In this paper, we provide a concept that conservatively
               decorrelates the estimates while bounding the unknown correlations
               as closely as possible. For this purpose, known independent quantities,
               such as measurement noise, are explicitly identified and exploited.
               Based on tight covariance bounds, the new approach allows for an
               intuitive and systematic derivation of appropriate tailor-made filter
               equations and does not require heuristics. Its performance is demonstrated
               in a comparative study within a typical SLAM scenario.},
  pdf       = {IFAC11_Noack.pdf}
}

@inproceedings{Fusion11_Baum-RHM,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {This paper is about tracking an extended object or a group target,
               which gives rise to a varying number of measurements from different
               measurement sources. For this purpose, the shape of the target is
               tracked in addition to its kinematics. The target extent is modeled
               with a new approach called Random Hypersurface Model (RHM) that assumes
               varying measurement sources to lie on scaled versions of the shape
               boundaries. In this paper, a star-convex RHM is introduced for tracking
               star-convex shape approximations of targets. Bayesian inference for
               star-convex RHM is performed by means of a Gaussian-assumed state
               estimator allowing for an efficient recursive closed-form measurement
               update. Simulations demonstrate the performance of this approach
               for typical extended object and group tracking scenarios.},
  annote    = {<b>Winner Best Student Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/BestStudentPaperAward_Fusion11.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {Fusion11_Baum-RHM.pdf}
}

@inproceedings{Fusion11_Baum-USF,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Using Symmetric State Transformations for Multi-Target Tracking}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {This paper is about the use of symmetric state transformations for
               multi-target tracking. First, a novel method for obtaining point
               estimates for multi-target states is proposed. The basic idea is
               to apply a symmetric state transformation to the original state in
               order to compute a minimum mean-square-error (MMSE) estimate in a
               transformed state. By this means, the known shortcomings of MMSE
               estimates for multi-target states can be avoided. Second, a new multi-target
               tracking method based on state transformations is suggested, which
               entirely performs the time and measurement update in a transformed
               space and thus, avoids the explicit calculation of data association
               hypotheses and removes the target identity from the estimation problem.
               The performance of the new approach is evaluated by means of tracking
               two crossing targets.},
  pdf       = {Fusion11_Baum-USF.pdf}
}

@inproceedings{Fusion11_Baum,
  author    = {Marcus Baum and Benjamin Noack and Frederik Beutler and Dominik Itte and Uwe D. Hanebeck},
  title     = {{Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {This paper is about tracking multiple targets with the so-called Symmetric
               Measurement Equation (SME) filter. The SME filter uses symmetric
               functions, e.g., symmetric polynomials, in order to remove the data
               association uncertainty from the measurement equation. By this means,
               the data association problem is converted to a nonlinear state estimation
               problem. In this work, an efficient optimal Gaussian filter based
               on analytic moment calculation for discrete-time multi-dimensional
               polynomial systems corrupted with Gaussian noise is derived, and
               then applied to the polynomial system resulting from the SME filter.
               The performance of the new method is compared to an UKF implementation
               by means of typical multiple target tracking scenarios.},
  pdf       = {Fusion11_Baum.pdf}
}

@inproceedings{Fusion11_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck},
  title     = {{Adaptive Model-Based Visual Stabilization of Image Sequences Using Feedback}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {Visual stabilization proposed in this paper compensates changes of
               the scene caused by motion and deformation of an observed object.
               This is of high importance in computer-assisted beating heart surgery,
               where the views of the beating heart should be stabilized. The proposed
               model-based method defines visual stabilization as a transformation
               of the current image sequence to a stabilized image sequence. This
               transformation incorporates physical model of the observed object
               and model of the measurement process. In contrast to standard approaches,
               the quality of the visual stabilization is continuously evaluated
               and improved in two aspects. On the one hand, discretization errors
               are reduced. On the other hand, the parameters of the underlying
               models are adjusted. The performance of the proposed method is evaluated
               in an experiment with a pressure-regulated artificial heart. Compared
               with standard methods, the model-based method provides higher accuracy,
               which is additionally improved by a feedback mechanism.},
  pdf       = {Fusion11_Bogatyrenko.pdf}
}

@inproceedings{Fusion11_Krauthausen,
  author    = {Peter Krauthausen and Patrick Ruoff and Uwe D. Hanebeck},
  title     = {{Sparse Mixture Conditional Density Estimation by Superficial Regularization}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {In this paper, the estimation of conditional densities between continuous
               random variables from noisy samples is considered. The conditional
               densities are modeled as heteroscedastic Gaussian mixture densities
               allowing for closed-form solution of Bayesian inference with full-densities.
               The main contributions of this paper are an improved generalization
               quality of the estimates by the introduction of a superficial regularizer,
               the consideration of model uncertainty relative to local data densities
               by means of adaptive covariances, and the proposition of an efficient
               distance-based estimation algorithm. This algorithm corresponds to
               an iterative nested optimization scheme, optimizing hyper-parameters,
               component placement, and mixture weights. The obtained solutions
               are sparse, smooth, and generalize well as benchmark experiments,
               e.g., in nonlinear filtering show.},
  pdf       = {Fusion11_Krauthausen.pdf}
}

@inproceedings{Fusion11_Noack,
  author    = {Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {Many modern fusion architectures are designed to process and fuse
               data in networked systems. Alongside the advantages, such as scalability and
               robustness, distributed fusion techniques particularly have to tackle the problem of dependencies
               between locally processed data. In linear estimation problems, uncertain
               quantities with unknown cross-correlations can be fused by means
               of the covariance intersection algorithm, which avoids overconfident
               fusion results. However, for nonlinear system dynamics and sensor
               models perturbed by arbitrary noise, it is not only a problem to
               characterize and parameterize dependencies between estimates, but
               also to find a proper notion of consistency. This paper addresses
               these issues by transforming the state estimates to a different state
               space, where the corresponding densities are Gaussian and only linear
               dependencies between estimates, i.e., correlations, can arise. These
               pseudo Gaussian densities then allow the notion of covariance consistency
               to be used in distributed nonlinear state estimation.},
  pdf       = {Fusion11_Noack.pdf}
}

@inproceedings{Fusion11_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {In data fusion theory, multiple estimates are combined to yield an
               optimal result. In this paper, the set of all possible results is
               investigated, when two random variables with unknown correlations
               are fused. As a first step, recursive processing of the set of estimates
               is examined. Besides set-theoretic considerations, the lack of knowledge
               about the unknown correlation coefficient is modeled as a stochastic
               quantity. Especially, a uniform model is analyzed, which provides
               a new optimization criterion for the covariance intersection algorithm
               in scalar state spaces. This approach is also generalized to multi-dimensional
               state spaces in an approximative, but fast and scalable way, so that
               consistent estimates are obtained.},
  pdf       = {Fusion11_Reinhardt.pdf}
}

@inproceedings{Fusion11_Ruoff,
  author    = {Patrick Ruoff and Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Progressive Correction for Deterministic Dirac Mixture Approximations}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {Since the advent of Monte-Carlo particle filtering, particle representations
               of densities have become increasingly popular due to their flexibility
               and implicit adaptive resolution. In this paper, an algorithm for
               the multiplication of a systematic Dirac mixture (DM) approximation
               with a continuous likelihood function is presented, which applies
               a progressive correction scheme, in order to avoid the particle degeneration
               problem. The preservation of sample regularity and therefore, representation
               quality of the underlying smooth density, is ensured by including
               a new measure of smoothness for Dirac mixtures, the DM energy, into
               the distance measure. A comparison to common correction schemes in
               Monte-Carlo methods reveals large improvements especially in cases
               of small overlap between the likelihood and prior density, as well
               as for multi-modal likelihoods.},
  pdf       = {Fusion11_Ruoff.pdf}
}

@inproceedings{ACC11_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck},
  title     = {{Visual Stabilization of Beating Heart Motion by Model-based Transformation of Image Sequences}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  abstract  = {In order to assist a surgeon by operating on a beating heart, visual
               stabilization makes the beating heart appear still to a surgeon by
               providing the current heart view as stationary and non-moving. In
               this way, the surgeon is not disturbed during an operation by a motion
               of the heart and can get an impression of performing conventional
               surgery. In contrast to existing methods for visual stabilization,
               the proposed approach involves a model-based transformation of image
               sequences provided by a camera system. This transformation incorporates
               the knowledge of physical characteristics of the heart in form of
               a mathematical model of the heart surface. Its main advantage is
               that the uncertainties of the model and measurements are considered.
               This occurs by estimating the parameters of the transformation. Furthermore,
               the quality of the visual stabilization is additionally improved
               by adapting the parameters of the underlying physical model. A performance
               of the proposed approach is evaluated in an experiment with a pressure-regulated
               artificial heart. In comparison to standard approaches, it provides
               superior results illustrating the high quality of the visual stabilization.},
  pdf       = {ACC11_Bogatyrenko.pdf}
}

@inproceedings{ACC11_Huber,
  author    = {Marco F. Huber and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Semi-Analytic Gaussian Assumed Density Filter}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  abstract  = {For Gaussian Assumed Density Filtering based on moment matching, a
               framework for the efficient calculation of posterior moments is proposed
               that exploits the structure of the given nonlinear system. The key
               idea is a careful discretization of some dimensions of the state
               space only in order to decompose the system into a set of nonlinear
               subsystems that are conditionally integrable in closed form. This
               approach is more efficient than full discretization approaches. In
               addition, the new decomposition is far more general than known Rao-Blackwellization
               approaches relying on conditionally linear subsystems. As a result,
               the new framework is applicable to a much larger class of nonlinear
               systems.},
  pdf       = {ACC11_Huber.pdf}
}

@inproceedings{ACC11_Krauthausen,
  author    = {Peter Krauthausen and Masoud Roschani and Uwe D. Hanebeck},
  title     = {{Incorporating Prior Knowledge into Nonparametric Conditional Density Estimation}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  abstract  = {In this paper, the problem of sparse nonparametric conditional density
               estimation based on samples and prior knowledge is addressed. The
               prior knowledge may be restricted to parts of the state space and
               given as generative models in form of mean-function constraints or
               as probabilistic models in the form of Gaussian mixtures. The key
               idea is the introduction of additional constraints and a modified
               kernel function into the conditional density estimation problem.
               This approach to using prior knowledge is a generic solution applicable
               to all nonparametric conditional density estimation approaches phrased
               as constrained optimization problems. The quality of the estimates,
               their sparseness, and the achievable improvements by using prior
               knowledge are shown in experiments for both Support-Vector Machine-based
               and integral distance-based conditional density estimation.},
  pdf       = {ACC11_Krauthausen.pdf}
}

@inproceedings{ACC11_Noack,
  author    = {Benjamin Noack and Daniel Lyons and Matthias Nagel and Uwe D. Hanebeck},
  title     = {{Nonlinear Information Filtering for Distributed Multisensor Data Fusion}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  abstract  = {The information filter has evolved into a key tool for distributed
               and decentralized multisensor estimation and control. Essentially,
               it is an algebraical reformulation of the Kalman filter and provides
               estimates on the information about an uncertain state rather than
               on a state itself. Whereas many practicable Kalman filtering techniques
               for nonlinear system and sensor models have been developed, approaches
               towards nonlinear information filtering are still scarce and limited.
               In order to deal with nonlinear systems and sensors, this paper derives
               an approximation technique for arbitrary probability densities that
               provides the same distributable fusion structure as the linear information
               filter. The presented approach not only constitutes a nonlinear version
               of the information filter, but it also points the direction to a
               Hilbert space structure on probability densities, whose vector space
               operations correspond to the fusion and weighting of information.},
  pdf       = {ACC11_Noack.pdf}
}

@inproceedings{WHC11_Perez,
  author    = {Antonia P{\'{e}}rez Arias and Henning P. Eberhardt and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{T}he {P}lenhaptic {G}uidance {F}unction for {I}ntuitive {N}avigation in {E}xtended {R}ange {T}elepresence {S}cenarios},
  booktitle = {Proceedings of the IEEE World Haptics Conference (WHC 2011)},
  year      = {2011},
  address   = {Istanbul, Turkey},
  month     = jun,
  abstract  = {In this work, we propose a plenhaptic guidance function that systematically
               describes the haptic information for guiding the user in the target
               environment. The plenhaptic guidance function defines the strength
               of the guidance at any position in space, at any direction, and at
               any time, and takes the geometry of the target environment as well
               as all possible goals into account. The plenhaptic guidance function,
               which can be rendered as active and passive guidance, is sampled
               and displayed to the user through a haptic interface in the user
               environment. The benefits of the plenhaptic guidance function for
               guiding the user to several simultaneous goals while avoiding the
               obstacles in a large target environment are demonstrated in real
               experiments.},
  pdf       = {WHC11_Perez.pdf}
}

@inproceedings{ARMS_AAMAS11_Lyons,
  author    = {Jan-P. Calliess and Daniel Lyons and Uwe D. Hanebeck},
  title     = {{Lazy auctions for multi-robot collision avoidance and motion control under uncertainty}},
  booktitle = {Autonomous Robots and Multirobot Systems (ARMS) Workshop at AAMAS 2011},
  year      = {2011},
  address   = {Taipei, Taiwan},
  month     = may,
  abstract  = {We present an auction-flavored multi-robot planning mechanism where
               coordination is to be achieved on the occupation of atomic resources
               modeled as binary inter-robot constraints. Introducing virtual obstacles,
               we show how this approach can be combined with particlebased obstacle
               avoidance methods, offering a decentralized, auction-based alternative
               to previously established centralized approaches for multirobot open-loop
               control. We illustrate the effectiveness of our new approach by presenting
               simulations of typical spatially-continuous multirobot path-planning
               problems and derive bounds on the collision probability in the presence
               of uncertainty.},
  pdf       = {ARMS11_CalliessLyons.pdf}
}

@inproceedings{EWSN11_Schmid,
  author    = {Johannes Schmid and Frederik Beutler and Benjamin Noack and Uwe D. Hanebeck and Klaus D. M{\"{u}}ller-Glaser},
  title     = {{An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks}},
  booktitle = {Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011)},
  publisher = {Springer},
  editor    = {Pedro Jos\'{e} Marr{\'{o}}n and Kamin Whitehouse},
  pages     = {147--162},
  volume    = {6567},
  year      = {2011},
  address   = {Bonn, Germany},
  month     = feb,
  abstract  = {In this paper, the localization of persons by means of a Wireless
               Sensor Network (WSN) is considered. Persons carry on-body sensor
               nodes and move within a WSN. The location of each person is calculated
               on this node and communicated through the network to a central data
               sink for visualization. Applications of such a system could be found in
               mass casualty events, firefighter scenarios, hospitals or retirement homes for example.
               For the location estimation on the sensor node, three derivatives of the
               Kalman Filter and a closed-form solution (CFS) are applied, compared,
               and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN
               is implemented and data are collected in in- and outdoor environments
               with differently positioned on-body nodes. The described estimators are
               then evaluated off-line on the experimentally collected data.
               The goal of this paper is to present a comprehensive real-world evaluation of methods for
               person localization in a WSN based on received signal strength (RSS) range measurements.
               It is concluded that person localization in in- and outdoor environments is possible
               under the considered conditions with the considered filters. The compared methods
               allow for suffciently accurate localization results and are robust against
               inaccurate range measurements.},
  doi       = {10.1007/978-3-642-19186-2_10},
  url       = {https://dx.doi.org/10.1007/978-3-642-19186-2_10},
  pdf       = {EWSN11_Schmid.pdf}
}

@inproceedings{CDC10_Hekler,
  author    = {Achim Hekler and Martin Kiefel and Uwe D. Hanebeck},
  title     = {{Nonlinear Bayesian Estimation with Compactly Supported Wavelets}},
  booktitle = {Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010)},
  year      = {2010},
  address   = {Atlanta, Georgia, USA},
  month     = dec,
  abstract  = {Bayesian estimation for nonlinear systems is still a challenging problem, as
               in general the type of the true probability density changes and the
               complexity increases over time. Hence, approximations of the occurring
               equations and/or of the underlying probability density functions are
               inevitable. In this paper, we propose an approximation of the conditional
               densities by wavelet expansions. This kind of representation allows a sparse
               set of characterizing coefficients, especially for smooth or piecewise
               smooth density functions. Besides its good approximation properties, fast
               algorithms operating on sparse vectors are applicable and thus, a good
               trade-off between approximation quality and run-time can be achieved.
               Moreover, due to its highly generic nature, it can be applied to a large
               class of nonlinear systems with a high modeling accuracy. In particular, the
               noise acting upon the system can be modeled by an arbitrary probability
               distribution and can influence the system in any way.},
  pdf       = {CDC10_HeklerKiefel.pdf}
}

@article{TM10_Noack,
  author   = {Benjamin Noack and Vesa Klumpp and Daniel Lyons and Uwe D. Hanebeck},
  title    = {{M}odellierung von {U}nsicherheiten und {Z}ustandssch{\"{a}}tzung mit {M}engen von {W}ahrscheinlichkeitsdichten},
  journal  = {tm -- Technisches Messen, Oldenbourg Verlag},
  year     = {2010},
  volume   = {77},
  pages    = {544--550},
  number   = {10},
  month    = oct,
  abstract = {Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
              Herausforderung in der Informationsfusion dar. Einerseits m{\"{u}}ssen
              geeignete Darstellungsformen f{\"{u}}r die Unsicherheiten bestimmt
              werden und andererseits darauf aufbauend effiziente Sch{\"{a}}tzverfahren
              hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
              mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser
              Beitrag stellt ein Verfahren zur Zustandssch{\"{a}}tzung vor, welches
              simultan stochastische und mengenbasierte Fehlergr{\"o}{\ss}en ber{\"u}cksichtigen
              kann, indem unsichere Gr{\"{o}}{\ss}en nicht mehr durch eine einzelne
              Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repr{\"{a}}sentiert
              werden. Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsm{\"{o}}glichkeiten
              dieser Unsicherheitsbeschreibung.},
  doi      = {10.1524/teme.2010.0087},
  editor   = {Klaus-Dieter Sommer and Fernando {Puente Le{\'{o}}n} and Michael Heizmann},
  pdf      = {TM10_Noack.pdf},
  url      = {https://dx.doi.org/10.1524/teme.2010.0087}
}

@article{TM10_Kuwertz,
  author   = {Achim Kuwertz and Marco F. Huber and Felix Sawo and Uwe D. Hanebeck},
  title    = {{Modellbasierte Quellenverfolgung in r{\"{a}}umlich ausgedehnten Ph{\"{a}}nomenen mittels Sensoreinsatzplanung}},
  journal  = {tm -- Technisches Messen, Oldenbourg Verlag},
  year     = {2010},
  volume   = {77},
  pages    = {551--557},
  number   = {10},
  month    = oct,
  abstract = {Bewegte Quellen k{\"{o}}nnen durch Emission r{\"{a}}umlich
              ausgedehnte Ph{\"{a}}nomene wie beispielsweise Schadstoff- oder
              Temperaturverteilungen erzeugen. Zur Lokalisierung von Quellen
              mit unbekannter Position stehen in vielen Aufgabenstellungen
              Informationen nur indirekt durch die verteilte Vermessung des
              induzierten Ph{\"{a}}nomens zur Verf{\"{u}}gung - etwa unter Verwendung
              station{\"{a}}rer oder mobiler Sensoren. Dieser Beitrag stellt
              modellbasierte Verfahren f{\"{u}}r eine echtzeitf{\"{a}}hige Lokalisierung
              und Verfolgung von bewegten Quellen vor. Zur gezielten Maximierung
              des Informationsgehalts der Messungen wird dabei eine vorausschauende
              Sensoreinsatzplanung genutzt, welche eine hohe Lokalisierungsg{\"{u}}te bei
              geringem Aufwand erm{\"{o}}glicht.},
  doi      = {10.1524/teme.2010.0088},
  editor   = {Klaus-Dieter Sommer and Fernando {Puente Le{\'o}n} and Michael Heizmann},
  pdf      = {TM10_Kuwertz.pdf},
  url      = {https://dx.doi.org/10.1524/teme.2010.0088}
}

@inproceedings{IROS10_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Optimal Stochastic Linearization for Range-based Localization}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  pdf       = {IROS10_Beutler.pdf},
  abstract  = {In range-based localization, the trajectory of a
               mobile object is estimated based on noisy range measurements
               between the object and known landmarks. In order to deal
               with this uncertain information, a Bayesian state estimator
               is presented, which exploits optimal stochastic linearization.
               Compared to standard state estimators like the Extended
               or Unscented Kalman Filter, where a point-based Gaussian
               approximation is used, the proposed approach considers the
               entire Gaussian density for linearization. By employing the common
               assumption that the state and measurements are jointly
               Gaussian, the linearization can be calculated in closed form
               and thus analytic expressions for the range-based localization
               problem can be derived.}
}

@inproceedings{IROS10_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  abstract  = {A reliable estimation of heart surface motion
               is an important prerequisite for the synchronization of surgical
               instruments in robotic beating heart surgery. In general, only
               an imprecise description of the heart dynamics and measurement
               systems is available. This means that the estimation of heart
               motion is corrupted by stochastic and systematic uncertainties.
               Without consideration of these uncertainties, the obtained results
               will be inaccurate and a safe robotic operation cannot be guaranteed.
               Until now, existing approaches for estimating the motion of the
               heart surface are either deterministic or treat only stochastic
               uncertainties. The proposed method extends the heart motion
               estimation to the simultaneous consideration of stochastic and
               unknown but bounded systematic uncertainties. It computes dynamic
               bounds in order to provide the surgeon with a guidance by
               constraining the motion of the surgical instruments and thereby
               protecting sensitive tissue.},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  pdf       = {IROS10_BogatyrenkoNoack.pdf}
}

@inproceedings{IROS10_Packi,
  author    = {Ferdinand Packi and Antonia P\'{e}rez Arias and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{A Wearable System for the Wireless Experience of Extended Range Telepresence}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  abstract  = {Extended range telepresence aims at enabling a
               user to experience virtual or remote environments, taking his
               own body movements as an input to define walking speed and
               viewing direction. Therefore, localization and tracking of the
               users pose (position and orientation) is necessary to perform
               a body-centered scene rendering. Visual and acoustic feedback
               is provided to the user by a head mounted display (HMD).
               To allow for free movement within the user environment, the
               tracking system is supposed to be user-wearable and entirely
               wireless. Consequently, a lightweight design is presented fea-
               turing small dimensions to fit into a conventional 13"laptop
               backpack, which satisfies the above stated demands for highly
               immersive extended range telepresence scenarios. Dedicated
               embedded hardware combined with off-the-shelf components
               is employed to form a robust, low-cost telepresence system that
               can be easily installed in any living room.},
  pdf       = {IROS10_PackiBeutler.pdf}
}

@inproceedings{IROS10_Perez,
  author    = {Antonia P{\'{e}}rez Arias and Uwe D. Hanebeck},
  title     = {{Wide-Area Haptic Guidance: Taking the User by the Hand}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  abstract  = {In this paper, we present a novel use of haptic
               information in extended range telepresence, the wide-area haptic
               guidance. It consists of force and position signals applied to the
               user's hand in order to improve safety, accuracy, and speed in
               some telepresent tasks. Wide-area haptic guidance assists the
               user in reaching a desired position in a remote environment of
               arbitrary size without degrading the feeling of presence. Several
               methods for haptic guidance are analyzed. With active haptic
               guidance, the user is guided by superimposed forces that pull
               him into the desired direction of motion, whereas under passive
               haptic guidance, the movement of the user is lightened in the
               preferred direction and constrained in the other directions. By
               using closed-loop haptic guidance instead of open-loop haptic
               guidance, not only is the user guided to his target but also
               the deviation from the desired target path is reduced. The
               proposed guidance methods were tested with a haptic interface
               specifically designed for extended range telepresence.},
  pdf       = {IROS10_Perez.pdf}
}

@inproceedings{IROS10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{A Model-Predictive Switching Approach To Efficient Intention Recognition}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  abstract  = {Estimating a user's intention is central to close
               human-robot cooperation. In this paper, the problem of per-
               forming intention recognition with tree-structured Dynamic
               Bayesian Networks for large environments with many features
               is addressed. The proposed approach reduces the computational
               complexity of inference O(b^s) for tree-structured measurement
               models with an average branching factor b and tree height s
               to O((b)s), where b~ << b. The key idea is to switch between a
               finite set of reduced system and measurement models in order
               to restrict inference to the most important features. A model
               predictive approach to online switching between the reduced
               models is proposed that exploits an upper bound of the distances
               of the reduced models to the full model. The effectiveness of
               the proposed algorithm is validated in the intention recognition
               for a humanoid robot using a telepresent household scenario.},
  address   = {Taipei, Taiwan},
  month     = oct,
  pdf       = {IROS10_Krauthausen.pdf}
}

@inproceedings{SDF10_Baum,
  author    = {Marcus Baum and Michael Feldmann and Dietrich Fr{\"{a}}nken and Uwe D. Hanebeck and Wolfgang Koch},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010)}},
  title     = {{Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models}},
  year      = {2010},
  address   = {Leipzig, Germany},
  month     = oct,
  abstract  = {Based on previous work of the authors, this paper provides a comparison
               of two different tracking methodologies for extended objects and
               group targets, where the true shape of the extent is approximated by
               an ellipsoid. Although both methods exploit usual sensor data, i.e.,
               position measurements of varying scattering centers, the distinctions
               are a consequence of the different modeling of the extent as a symmetric
               positive definite random matrix on the one hand and an elliptic random
               hypersurface model on the other. Besides analyzing the fundamental
               assumptions and a comparison of the properties of these tracking methods,
               simulation results are presented based on a static tracking environment
               to highlight especially the differences in the update step for the
               extension estimate.},
  pdf       = {SDF10_BaumFeldmann.pdf}
}

@article{IJCARS10_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Pascal Pompey and Uwe D. Hanebeck},
  title     = {{Efficient Physics-Based Tracking of Heart Surface Motion for Beating Heart Surgery Robotic Systems}},
  journal   = {International Journal of Computer Assisted Radiology and Surgery (IJCARS 2010)},
  year      = {2010},
  month     = aug,
  pages     = {387--399},
  doi       = {doi:10.1007/s11548-010-0517-5},
  volume    = {6},
  number    = {3},
  publisher = {Springer Berlin / Heidelberg},
  issn      = {1861-6410},
  pdf       = {IJCARS10_Bogatyrenko.pdf},
  abstract  = {Purpose: Tracking of beating heart motion in a robotic
               surgery system is required for complex cardiovascular interventions.
               Methods: A heart surface motion tracking method is developed,
               including a stochastic physics-based heart surface
               model and an efficient reconstruction algorithm. The algorithm
               uses the constraints provided by the model that exploits
               the physical characteristics of the heart. The main advantage
               of the model is that it is more realistic than most standard
               heartmodels. Additionally, no explicit matching between the
               measurements and the model is required. The application of
               meshless methods significantly reduces the complexity of
               physics-based tracking.
               Results: Based on the stochastic physical model of the heart
               surface, this approach considers the motion of the intervention
               area and is robust to occlusions and reflections. The
               tracking algorithm is evaluated in simulations and experiments
               on an artificial heart. Providing higher accuracy than
               the standardmodel-based methods, it successfully copes with
               occlusions and provides high performance even when all
               measurements are not available.
               Conclusions: Combining the physical and stochastic description
               of the heart surface motion ensures physically correct
               and accurate prediction. Automatic initialization of the physics-based
               cardiac motion tracking enables system evaluation
               in a clinical environment.},
  url       = {https://dx.doi.org/10.1007/s11548-010-0517-5},
  note      = {doi:10.1007/s11548-010-0517-5}
}

@book{KI10_Hanebeck,
  title     = {KI 2010: Advances in Artificial Intelligence, Proceedings of the 33rd Annual German Conference on AI, Karlsruhe, Germany},
  publisher = {Springer},
  year      = {2010},
  editor    = {R{\"{u}}diger Dillmann and J{\"{u}}rgen Beyerer and Uwe D. Hanebeck and Tanja Schultz},
  series    = {Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence},
  month     = sep
}

@inproceedings{KI10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Situation-Specific Intention Recognition for Human-Robot-Cooperation}},
  booktitle = {{33rd Annual German Conference on Artificial Intelligence (KI 2010)}},
  publisher = {Gesellschaft f{\"u}r Informatik e.V.},
  year      = {2010},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {KI10_Krauthausen.pdf},
  abstract  = {Recognizing human intentions is part of the decision
               process in many technical devices. In order to achieve
               natural interaction, the required estimation quality and
               the used computation time need to be balanced. This becomes
               challenging, if the number of sensors is high and measurement
               systems are complex. In this paper, a model predictive approach
               to this problem based on online switching of small,
               situation-specific Dynamic Bayesian Networks is proposed.
               The contributions are an efficient modeling and inference
               of situations and a greedy model predictive switching algorithm
               maximizing the mutual information of predicted situations. The
               achievable accuracy and computational savings are demonstrated
               for a household scenario by using an extended range telepresence system.}
}

@inproceedings{IPIN10_Packi,
  author    = {Ferdinand Packi and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Wireless Acoustic Tracking for Extended Range Telepresence}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010)},
  year      = {2010},
  address   = {Z{\"u}rich, Switzerland},
  month     = sep,
  pdf       = {IPIN10_Packi.pdf},
  abstract  = {Telepresence systems enable a user to experience
               virtual or distant environments by providing sensory feedback.
               Appropriate devices include head mounted displays (HMD) for
               visual perception, headphones for auditory response, or even
               haptic displays for tactile sensation and force feedback. While
               most common designs use dedicated input devices like joysticks
               or a space mouse, the approach followed in the present work
               takes the user's position and viewing direction as an input, as he
               walks freely in his local surroundings. This is achieved by using
               acoustic tracking, where the user's pose (position and orientation)
               is estimated on the basis of ranges measured between a set
               of wall-fastened loudspeakers and a microphone array fixed on
               the user's HMD. To allow for natural user motion, a wearable,
               fully wireless telepresence system is introduced. The increase in
               comfort compared to wired solutions is obvious, as the user's
               awareness of distracting cables is taken away during walking.
               Also the lightweight design and small dimensions contribute to
               ergonomics, as the whole assembly fits well into a small backpack.}
}

@inproceedings{VECIMS10_Baum,
  author    = {Ioana Gheta and Marcus Baum and Andrey Belkin and J{\"{u}}rgen Beyerer and Uwe D. Hanebeck},
  booktitle = {{Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010)}},
  title     = {{Three Pillar Information Management System for Modeling the Environment of Autonomous Systems}},
  year      = {2010},
  address   = {Taranto, Italy},
  month     = sep,
  abstract  = {This contribution is about an information management and storage system for modeling the
               environment of autonomous systems. The three pillars of the system consist of prior
               knowledge, environment model and sensory information. The main pillar is the environment model,
               which supplies the autonomous system with relevant information about its current environment.
               For this purpose, an abstract representation of the real world is created, where instances
               with attributes and relations serve as virtual substitutes of entities (persons and objects)
               of the real world. The environment model is created based on sensory information about
               the real world. The gathered sensory information is typically uncertain in a stochastic
               sense and is represented in the environment model by means of Degree-of-Belief (DoB) distributions.
               The prior knowledge contains all relevant background knowledge (e.g., concepts organized in ontologies)
               for creating and maintaining the environment model. The concept of the three pillar information system
               has previously been published. Therefore this contribution focuses on further central properties
               of the system. Furthermore, the development status and possible applications as well as evaluation
               scenarios are discussed.},
  pdf       = {VECIMS10_GhetaBaum.pdf}
}

@inproceedings{ASE10_Baum,
  author    = {Reiner H\"{a}hnle and Marcus Baum and Richard Bubel and Marcel Rothe},
  title     = {{A Visual Interactive Debugger Based on Symbolic Execution}},
  booktitle = {Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010)},
  year      = {2010},
  address   = {Antwerp, Belgium},
  month     = sep
}


@inproceedings{MFI10_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Association-free Tracking of Two Closely Spaced Targets}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_Baum.pdf},
  abstract  = {This paper introduces a new concept for tracking closely spaced targets in Cartesian
               space based on position measurements corrupted with additive Gaussian noise.
               The basic idea is to select a special state representation that eliminates the target identity
               and avoids the explicit evaluation of association probabilities.
               One major advantage of this approach is that the resulting likelihood function for this special problem is unimodal.
               Hence, it is especially suitable for closely spaced targets.
               The resulting estimation problem can be tackled with a standard nonlinear estimator.
               In this work, we focus on two targets in two-dimensional Cartesian space.
               The Cartesian coordinates of the targets are represented by means of  extreme values, i.e.,
               minima and maxima. Simulation results demonstrate the feasibility of the new approach.}
}

@inproceedings{MFI10_BaumRect,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking a Minimum Bounding Rectangle based on Extreme Value Theory}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_BaumRect.pdf},
  abstract  = {In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is
               derived. Each given measurement stems from an unknown point  and is corrupted with additive Gaussian noise.
               Extreme value theory is applied in order to derive a linear measurement equation for the problem.
               The new estimator is applied to the problem of group target and extended object tracking.
               Instead of estimating each single group member or point feature explicitly, the basic idea is to track a summarizing shape, namely the minimum bounding rectangle, of the
               group. Simulation results demonstrate the feasibility of the estimator.}
}

@inproceedings{MFI10_BaumGheta,
  author    = {Marcus Baum and Ioana Gheta and Andrey Belkin and J{\"u}rgen Beyerer and Uwe D. Hanebeck},
  title     = {{Data Association in a World Model for Autonomous Systems}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_BaumGheta.pdf},
  abstract  = {This contribution introduces a three pillar information storage and management
               system for modeling the environment of autonomous systems. The main
               characteristics is the separation of prior knowledge, environment model and
               sensor information. In the center of the system is the environment model, which
               provides the autonomous system with information about the current state of the
               environment. It consists of instances with attributes and relations as virtual
               substitutes of entities (persons and objects) of the real world.
               Important features are the representation of uncertain information by means
               of Degree-of-Belief (DoB) distributions,
               the information exchange between the three pillars as well as creation,
               deletion and update of instances, attributes and relations in the environment
               model. In this work, a Bayesian method for fusing new observations to the
               environment model is introduced. For this purpose, a Bayesian data association
               method is derived. The main question answered here is the
               observation-to-instance mapping
               and the decision mechanisms for creating a new instance or
               updating already existing instances in the environment model.}
}

@inproceedings{MFI10_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  abstract  = {In range-based pose tracking, the translation and
               rotation of an object with respect to a global coordinate system
               has to be estimated. The ranges are measured between the
               target and the global frame. In this paper, an intelligent decomposition
               is introduced in order to reduce the computational
               effort for pose tracking. Usually, decomposition procedures only
               exploit conditionally linear models. In this paper, this principle
               is generalized to conditionally integrable substructures and
               applied to pose tracking. Due to a modified measurement
               equation, parts of the problem can even be solved analytically.},
  pdf       = {MFI10_Beutler.pdf}
}

@inproceedings{MFI10_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck},
  title     = {{Simultaneous State and Parameter Estimation for Physics-Based Tracking of Heart Surface Motion}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_Bogatyrenko.pdf},
  abstract  = {Most existing approaches for tracking of the
               beating heart motion assume known cardiac kinematics and
               material parameters. However, these assumptions are not realistic
               for application in beating heart surgery. In this paper,
               a novel probabilistic tracking approach based on a physical
               model of the heart surface is presented. In contrast to existing
               approaches, the physical information about heart kinematics
               and material properties is incorporated and considered in
               an estimation of the heart behavior. An additional advantage
               is that the time-dependencies and uncertainties of the heart
               parameters are efficiently handled by exploiting simultaneous
               state and parameter estimation. Furthermore, by decomposing
               the state into linear and nonlinear substructures, the computational
               complexity of the estimation problem is reduced. The
               experimental results demonstrate the high performance of the
               method proposed in this paper. The solution of the parameter
               identification problem allows a personalized physical model and
               opens up possibilities to apply the physics-based tracking of the
               heart surface motion in a clinical environment.}
}

@inproceedings{MFI10_KrauthausenEberhardt,
  author    = {Peter Krauthausen and Henning Eberhardt and Uwe D. Hanebeck},
  title     = {{Multivariate Parametric Density Estimation Based On The Modified Cram\'{e}r-von Mises Distance}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  abstract  = {In this paper, a novel distance-based density
               estimation method is proposed, which considers the overall
               density function in the goodness-of-fit. In detail, the parameters
               of Gaussian mixture densities are estimated from samples,
               based on the distance of the cumulative distributions over
               the entire state space. Due to the ambiguous definition of the
               standard multivariate cumulative distribution, the Localized
               Cumulative Distribution and a modified Cram\'{e}r-von Mises
               distance measure are employed. A further contribution is the
               derivation of a simple-to-implement optimization procedure
               for the optimization problem. The proposed approach's good
               performance in estimating arbitrary Gaussian mixture densities
               is shown in an experimental comparison to the Expectation
               Maximization algorithm for Gaussian mixture densities.},
  pdf       = {MFI10_KrauthausenEberhardt.pdf}
}

@inproceedings{MFI10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Regularized Non-Parametric Multivariate Density and Conditional Density Estimation}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  abstract  = {In this paper, a distance-based method for both
               multivariate non-parametric density and conditional density
               estimation is proposed. The contributions are the formulation
               of both density estimation problems as weight optimization
               problems for Gaussian mixtures centered about samples with
               identical parameters. Furthermore, the minimization is based
               on the modified Cram\'{e}r-von Mises distance of the Localized
               Cumulative Distributions, removing the ambiguity of the defi-
               nition of the multivariate cumulative distribution function. The
               minimization problem is amended with a regularization term
               penalizing the densities' roughness to avoid overfitting. The
               resulting estimation problems for both densities and conditional
               densities are shown to be phrasable in the form of readily
               implementable quadratic programs. Experimental comparison
               against EM, SVR, and GPR based on the log-likelihood and
               performance in benchmark recursive filtering applications show
               high quality of the densities and good performance at less
               computational cost, i.e., the density representations are sparser.},
  pdf       = {MFI10_Krauthausen.pdf}
}

@inproceedings{MFI10_LyonsHekler,
  author    = {Daniel Lyons and Achim Hekler and Markus Kuderer and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  title     = {{Robust Model Predictive Control with Least Favorable Measurements}},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  abstract  = {Closed-loop model predictive control of nonlinear systems,
               whose internal states are not completely accessible, incorporates
               the impact of possible future measurements into the planning
               process. When planning ahead in time, those measurements
               are not known, so the closed-loop controller accounts for
               the expected impact of all potential measurements. We propose a novel
               conservative closed-loop control approach that does not calculate the
               expected impact of all measurements, but solely considers the single
               future measurement that has the worst impact on the control objective.
               In doing so, the model predictive controller guarantees robustness
               even in the face of high disturbances acting upon the system. Moreover,
               by considering only a single dedicated measurement, the complexity of
               closed-loop control is reduced significantly. The capabilities of our
               approach are evaluated by means of a path planning problem for a mobile robot.},
  annote    = {<b>Nominee Best Paper Award</b>},
  pdf       = {MFI10_LyonsHekler.pdf}
}

@inproceedings{MSC10_HeklerLyonsNoack,
  author    = {Achim Hekler and Daniel Lyons and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities}},
  booktitle = {Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010)},
  address   = {Yokohama, Japan},
  year      = {2010},
  month     = sep,
  abstract  = {In Model Predictive Control, the quality of control
               is highly dependent upon the model of the system under control.
               Therefore, a precise deterministic model is desirable. However,
               in real-world applications, modeling accuracy is typically limited
               and systems are generally affected by disturbances. Hence,
               it is important to systematically consider these uncertainties
               and to model them correctly. In this paper, we present a
               novel Nonlinear Model Predictive Control method for systems
               affected by two different types of perturbations that are
               modeled as being either stochastic or unknown but bounded
               quantities. We derive a formal generalization of the Nonlinear
               Model Predictive Control principle for considering both types
               of uncertainties simultaneously, which is achieved by using
               sets of probability densities. In doing so, a more robust and
               reliable control is obtained. The capabilities and benefits of
               our approach are demonstrated in real-world experiments with
               miniature walking robots.},
  pdf       = {MSC10_HeklerLyonsNoack.pdf}
}

@inproceedings{Fusion10_BaumKlumpp,
  author    = {Marcus Baum and Vesa Klumpp and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{A Novel Bayesian Method for Fitting a Circle to Noisy Points}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  abstract  = {This paper introduces a novel recursive Bayesian
               estimator for the center and radius of a circle based on
               noisy points. Each given point is assumed to be a noisy measurement
               of an unknown true point on the circle that is corrupted with known
               isotropic Gaussian noise. In contrast to existing approaches, the
               novel method does not make  assumptions about the  true points on
               the circle, where the measurements stem from. Closed-form expressions
               for the measurement update step are derived. Simulations show that
               the novel method outperforms standard Bayesian approaches for
               circle fitting.},
  pdf       = {Fusion10_BaumKlumpp.pdf}
}

@inproceedings{Fusion10_BaumNoack,
  author    = {Marcus Baum and Benjamin Noack and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{Extended Object and Group Tracking with Elliptic Random Hypersurface Models}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  abstract  = {This paper provides new results and
               insights for tracking an  extended target object
               modeled with an  Elliptic Random Hypersurface Model (RHM).
               An Elliptic RHM specifies the relative squared Mahalanobis
               distance of a measurement source to the center of the
               target object by means of a one-dimensional random scaling
               factor. It is shown that uniformly distributed measurement
               sources on an ellipse lead to a uniformly distributed
               squared scaling factor. Furthermore, a Bayesian inference
               mechanisms tailored to elliptic shapes is introduced, which
               is also suitable for scenarios with high measurement noise.
               Closed-form expressions for the measurement update in case
               of Gaussian and uniformly distributed squared scaling factors are derived.},
  pdf       = {Fusion10_BaumNoack.pdf}
}

@inproceedings{Fusion10_Beutler,
  author    = {Frederik Beutler and Uwe D. Hanebeck},
  title     = {{A Two-Step Approach for Offset and Position Estimation from Pseudo-Ranges Applied to Multilateration Tracking}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  pdf       = {Fusion10_Beutler.pdf},
  abstract  = {In multilateration tracking, an object, e.g., an airplane, emits a known
               reference signal, which is received by several base stations (sensors) located at
               known positions. The receiving times of the signal at the sensors correspond to the times of
               arrival (TOA) plus an unknown offset, because the emission time is unknown.
               Usually, for estimating the position of the object, the receiving times are
               converted to a larger number of time differences of arrival (TDOA) in order
               to eliminate the unknown offset. To avoid this conversion, the proposed
               approach directly uses the receiving times. This is achieved by 1. determining the optimal offset from the redundant measurements in closed
               form and 2. by considering a modified measurement equation. As a result,
               position estimation can be performed by optimal stochastic linearization.},
  month     = jul
}

@inproceedings{Fusion10_DunauPacki,
  author    = {Patrick Dunau and Ferdinand Packi and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Efficient Multilateration Tracking with Concurrent Offset Estimation using Stochastic Filtering Techniques}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  abstract  = {Multilateration systems operate by deter-
               mining distances between a signal transmitter and a
               number of receivers. In aerial surveillance, radio sig-
               nals are emitted as Secondary Surveillance Radar (SSR)
               by the aircraft, representing the signal transmitter. A
               number of base stations (sensors) receive the signals at
               different times. Most common approaches use time dif-
               ference of arrival (TDOA) measurements, calculated by
               subtracting receiving times of one receiver from another.
               As TDOAs require intersecting hyperboloids, which is
               considered a hard task, this paper follows a different ap-
               proach, using raw receiving times. Thus, estimating the
               signal's emission time is required, captured as a com-
               mon offset within an augmented version of the system
               state. This way, the multilateration problem is reduced
               to intersecting cones. Estimation of the aircraft's posi-
               tion based on a nonlinear measurement model and an
               underlying linear system model is achieved using a lin-
               ear regression Kalman filter [1, 2]. A decomposed com-
               putation of the filter step is introduced, allowing a more
               efficient calculation.},
  pdf       = {Fusion10_DunauPacki.pdf},
  month     = jul
}

@inproceedings{Fusion10_EberhardtKlumpp,
  author    = {Henning Eberhardt and Vesa Klumpp and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{Density Trees for Efficient Nonlinear State Estimation}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  abstract  = {In this paper, a new class of nonlinear Bayesian
               estimators based on a special space partitioning structure, generalized
               Octrees, is presented. This structure minimizes memory and calculation
               overhead. It is used as a container framework for a set of node functions
               that approximate a density piecewise. All necessary operations are derived
               in a very general way in order to allow for a great variety of Bayesian
               estimators. The presented estimators are especially well suited for
               multi-modal nonlinear estimation problems. The running time performance
               of the resulting estimators is first analyzed theoretically and then backed
               by means of simulations. All operations have a linear running time in
               the number of tree nodes.},
  pdf       = {Fusion10_EberhardtKlumpp.pdf}
}

@inproceedings{Fusion10_Klumpp-SGMF,
  author    = {Vesa Klumpp and Frederik Beutler and Uwe D. Hanebeck and Dietrich Fr{\"a}nken},
  title     = {{The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  abstract  = {In this paper, a novel nonlinear/non-linear model
               decomposition for the Sliced Gaussian Mixture Filter is presented.
               Based on the level of nonlinearity of the model, the overall estimation
               problem is decomposed into a severely nonlinear and a slightly
               nonlinear part, which are processed by different estimation techniques.
               To further improve the efficiency of the estimator, an adaptive state
               decomposition algorithm is introduced that allows decomposition
               according to the linearization error for nonlinear system and
               measurement models. Simulations show that this approach has orders of
               magnitude less complexity compared to other state of the art
               estimators, while maintaining comparable estimation errors.},
  pdf       = {Fusion10_Klumpp-SGMF.pdf},
  month     = jul
}

@inproceedings{Fusion10_Klumpp,
  author    = {Vesa Klumpp and Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  abstract  = {In estimation theory, mainly set-theoretic or
               stochastic uncertainty is considered. In some cases, especially when
               some statistics of a distribution are not known or additional
               stochastic information is used in a set-theoretic estimator, both
               types of uncertainty have to be considered. In this paper, two
               estimators that cope with combined stoachastic and set-theoretic
               uncertainty are compared, namely the Set-theoretic and Statistical
               Information filter, which represents the uncertainty by means of
               random sets, and the Credal State filter, in which the state
               information is given by sets of probability density functions.
               The different uncertainty assessment in both estimators leads to
               different estimation results, even when the prior information and
               the measurement and system models are equal. This paper explains
               these differences and states directions, when which estimator
               should be applied to a given estimation problem.},
  pdf       = {Fusion10_Klumpp.pdf},
  month     = jul
}

@inproceedings{Fusion10_KrauthausenHuber,
  author    = {Peter Krauthausen and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Support-Vector Conditional Density Estimation for Nonlinear Filtering}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  pdf       = {Fusion10_KrauthausenHuber.pdf},
  abstract  = {A non-parametric conditional density
               estimation algorithm for nonlinear stochastic dynamic
               systems is proposed. The contributions are a novel sup-
               port vector regression for estimating conditional den-
               sities, modeled by Gaussian mixture densities, and an
               algorithm based on cross-validation for automatically
               determining hyper-parameters for the regression. The
               conditional densities are employed with a modi?ed axis-
               aligned Gaussian mixture filter. The experimental va-
               lidation shows the high quality of the conditional densi-
               ties and good accuracy of the proposed filter.},
  month     = jul
}

@inproceedings{Fusion10_Noack,
  author    = {Benjamin Noack and Vesa Klumpp and Nikolay Petkov and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  abstract  = {Applying the Kalman filtering scheme to linearized system dynamics and observation models does in general not yield optimal state estimates.
               More precisely, inconsistent state estimates and covariance matrices are caused by neglected linearization errors.
               This paper introduces a concept for systematically predicting and updating bounds for the linearization errors within the Kalman filtering framework.
               To achieve this, an uncertain quantity is not characterized by a single probability density anymore, but rather by a set of densities and accordingly,
               the linear estimation framework is generalized in order to process sets of probability densities. By means of this generalization,
               the Kalman filter may then not only be applied to stochastic quantities, but also to unknown but bounded quantities.
               In order to improve the reliability of Kalman filtering results, the last-mentioned quantities are utilized to bound the typically neglected nonlinear parts of a linearized mapping.},
  pdf       = {Fusion10_Noack.pdf}
}

@inproceedings{SUTC10_Lyons,
  author    = {Daniel Lyons and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{A Log-Ratio Information Measure for Stochastic Sensor Management}},
  booktitle = {{Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010)}},
  year      = {2010},
  address   = {Newport Beach, California, USA},
  month     = jun,
  pdf       = {SUTC10_Lyons.pdf},
  abstract  = {In distributed sensor networks, computational and energy resources are
               in general limited.  Therefore, an intelligent selection of sensors for
               measurements is of great importance to ensure both high estimation
               quality and an extended lifetime of the network. Methods from the theory
               of model predictive control together with information theoretic measures
               have been employed to pick sensors yielding measurements with high
               information value. We present a novel information measure that originates from a
               scalar product on a class of continuous probability densities and apply it
               to the field of sensor management. Aside from its mathematical justifications
               for quantifying the information content of probability densities, the most
               remarkable property of the measure, an analogon of the triangle inequality
               under Bayesian information fusion, is deduced. This allows for deriving
               computationally cheap upper bounds for the model predictive sensor selection
               algorithm and for comparing the performance of planning over different lengths of time horizons.}
}

@inproceedings{ACC10_Eberhardt,
  author    = {Henning Eberhardt and Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Optimal Dirac Approximation by Exploiting Independencies}},
  booktitle = {{Proceedings of the 2010 American Control Conference (ACC 2010)}},
  year      = {2010},
  address   = {Baltimore, Maryland, USA},
  month     = jun,
  keywords  = {Stochastic systems, Filtering},
  abstract  = {The sample-based recursive prediction of discrete-time nonlinear
               stochastic dynamic systems requires a regular reapproximation of the Dirac mixture
               densities characterizing the state estimate with an exponentially increasing number
               of components. For that purpose, a systematic approximation method is proposed that
               is deterministic and guaranteed to minimize a new type distance measure, the so
               called modified Cram\'{e}r-von Mises distance. A huge increase in approximation
               performance is achieved by exploiting structural independencies usually occurring
               between the random variables used as input to the system. The corresponding prediction
               step achieves optimal performance when no further assumptions can be made about the
               system function. In addition, the proposed approach shows a much better convergence
               compared to the prediction step of the particle filter and by far fewer Dirac components
               are required for achieving a given approximation quality. As a result, the new
               approximation method opens the way for the development of new fully deterministic and
               optimal stochastic state estimators for nonlinear dynamic systems.},
  pdf       = {ACC10_EberhardtKlumpp.pdf}
}

@inproceedings{ACC10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Parameter Learning for Hybrid Bayesian Networks With Gaussian Mixture and Dirac Mixture Conditional Densities}},
  booktitle = {{Proceedings of the 2010 American Control Conference (ACC 2010)}},
  year      = {2010},
  address   = {Baltimore, Maryland, USA},
  month     = jun,
  abstract  = {In this paper, the first algorithm for learning hybrid Bayesian
               Networks with Gaussian mixture and Dirac mixture conditional densities from data
               given their structure is presented. The mixture densities to be learned allow for
               nonlinear dependencies between the variables and exact closedform inference. For
               learning the network's parameters, an incremental gradient ascent algorithm is derived.
               Analytic expressions for the partial derivatives and their combination with messages are
               presented. This hybrid approach subsumes the existing approach for purely discrete-valued
               networks and is applicable to partially observable networks, too. Its practicability is
               demonstrated by a reference example.},
  pdf       = {ACC10_Krauthausen.pdf}
}

@incollection{VMS10_Lyons,
  author    = {Daniel Lyons and Achim Hekler and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Ma{\ss}e f{\"u}r Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung}},
  booktitle = {Verteilte Messsysteme},
  publisher = {KIT Scientific Publishing},
  year      = {2010},
  editor    = {Fernando {Puente Le{\'{o}}n} and Klaus-Dieter Sommer and Michael Heizmann},
  pages     = {121--132},
  month     = mar,
  abstract  = {Bei der Beobachtung eines r{\"a}umlich verteilten Ph{\"a}nomens mit einer
               Vielzahl von Sensoren ist die intelligente Auswahl von Messkonfigurationen aufgrund von
               beschr{\"a}nkten Rechen- und Kommunikationskapazit{\"a}ten entscheidend f{\"u}r die
               Lebensdauer des Sensornetzes. Mit der Sensoreinsatzplanung kann die im n{\"a}chsten
               Zeitschritt anzusteuernde Messkonfiguration dynamisch mittels einer stochastischen
               modell-pr{\"a}diktiven Planung {\"u}ber einen endlichen Zeithorizont bestimmt werden.
               Dabei wird als G{\"u}tekriterium die Maximierung des zu erwartenden Informationsgewinns
               durch zuk{\"u}nftige Messungen unter sparsamer Verwendung der Energieressourcen gew{\"a}hlt.
               In diesem Artikel wird ein neues Ma{\ss} f{\"u}r kontinuierliche Wahrscheinlichkeitsdichten
               vorgestellt, das sich kanonisch aus der Konstruktion eines Vektorraums f{\"u}r
               Wahrscheinlichkeitsdichten ergibt. Dieses Ma{\ss} wird als G{\"u}tefunktion in der
               vorausschauenden Sensoreinsatzplanung zur Bewertung des informationstheoretischen Einflu{\ss}
               von Messungen auf die aktuelle Zustandssch{\"a}tzung verwendet.},
  url       = {https://publikationen.bibliothek.kit.edu/1000015670}
}

@incollection{VMS10_Noack,
  author    = {Benjamin Noack and Vesa Klumpp and Daniel Lyons and Uwe D. Hanebeck},
  title     = {{S}ystematische {B}eschreibung von {U}nsicherheiten in der {I}nformationsfusion mit {M}engen von {W}ahrscheinlichkeitsdichten},
  booktitle = {Verteilte Messsysteme},
  publisher = {KIT Scientific Publishing},
  year      = {2010},
  editor    = {Fernando {Puente Le{\'{o}}n} and Klaus-Dieter Sommer and Michael Heizmann},
  pages     = {167--178},
  month     = mar,
  abstract  = {Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
               Herausforderung in der Informationsfusion dar. Einerseits m{\"u}ssen geeignete Darstellungsformen
               f{\"u}r die Unsicherheiten bestimmt werden und andererseits darauf aufbauend effiziente
               Sch{\"a}tzverfahren hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
               mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser Beitrag stellt ein Verfahren
               zur Zustandssch{\"a}tzung vor, welches simultan stochastische und mengenbasierte Fehlergr{\"o}{\ss}en
               ber{\"u}cksichtigen kann, indem unsichere Gr{\"o}{\ss}en nicht mehr durch eine einzelne
               Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repr{\"a}sentiert werden.
               Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsm{\"o}glichkeiten dieser
               Unsicherheitsbeschreibung.},
  url       = {https://publikationen.bibliothek.kit.edu/1000015670}
}

@incollection{VMS10_Kuwertz,
  author    = {Achim Kuwertz and Marco F. Huber and Felix Sawo and Uwe D. Hanebeck},
  title     = {{Sensoreinsatzplanung zur Verfolgung von Quellen r{\"a}umlich ausgedehnter Ph{\"a}nomene}},
  booktitle = {Verteilte Messsysteme},
  publisher = {KIT Scientific Publishing},
  year      = {2010},
  editor    = {Fernando {Puente Le{\'{o}}n} and Klaus-Dieter Sommer and Michael Heizmann},
  pages     = {179--191},
  month     = mar,
  abstract  = {R{\"a}umlich ausgedehnte Ph{\"a}nomene wie Schadstoffverteilungen in Gew{\"a}ssern
               oder Temperaturverteilungen in R{\"a}umen werden vielfach durch unbekannte, aber gegebenenfalls
               sich bewegende Quellen erzeugt. Allerdings stehen in vielen praktisch relevanten Aufgabenstellungen
               Informationen zur Lokalisierung einer derartigen Quelle nur indirekt durch eine Vermessung des
               induzierten Ph{\"a}nomens zur Verf{\"u}gung, welche den Einsatz eines verteilten Messsystems erfordert.
               Die Messungen k{\"o}nnen dabei beispielsweise von einem station{\"a}ren Sensornetz oder von mobilen
               Sensoren stammen. In diesem Beitrag werden modellbasierte Verfahren zu echtzeitf{\"a}higen Lokalisierung
               und schritthaltenden Verfolgung von Quellen vorgestellt, welche gezielt r{\"a}umlich und zeitlich
               verteilte Messungen einsetzen. Um den Informationsgewinn und somit den Nutzen verteilter Messungen zu
               maximieren, spielt bei diesem Verfahren neben einer mathematischen Modellierung auch eine vorausschauende
               Sensoreinsatzplanung eine zentrale Rolle. Das in diesem Beitrag vorgeschlagene Planungsverfahren
               erm{\"o}glicht dabei die effiziente und ressourcenschonende Verfolgung beweglicher Quellen bei gleichzeitig
               hoher Lokalisierungsgenauigkeit.},
  url       = {https://publikationen.bibliothek.kit.edu/1000015670}
}

@inproceedings{PED10_Perez,
  author    = {Antonia P\'{e}rez Arias and Tobias Kretz and Peter Ehrhardt and Stefan Hengst and Peter Vortisch and Uwe D. Hanebeck},
  title     = {{Extended Range Telepresence for Evacuation Training in Pedestrian Simulations}},
  booktitle = {{Proccedings of the 5th International Conference on Pedestrian and Evacuation Dynamics (PED 2010), Springer-Verlag}},
  year      = {2010},
  address   = {Gaithersburg, Maryland, USA},
  month     = mar,
  abstract  = {In this contribution, we propose a new framework to evaluate pedestrian
               simulations by using Extended Range Telepresence. Telepresence is
               used as a virtual reality walking simulator, which provides the user
               with a realistic impression of being present and walking in a virtual
               environment that is much larger than the real physical environment,
               in which the user actually walks. The validation of the simulation
               is performed by comparing motion data of the telepresent user with
               simulated data at some points of the simulation. The use of haptic
               feedback from the simulation makes the framework suitable for training
               in emergency situations.},
  pdf       = {PED10_Perez.pdf}
}

@inproceedings{NIPS09_Deisenroth,
  author    = {Ryan Turner and Marc Peter Deisenroth and Carl Edward Rasmussen},
  title     = {{System Identification in Gaussian Process Dynamical Systems}},
  booktitle = {Nonparametric Bayes Workshop at NIPS 2009},
  year      = {2009},
  address   = {Whistler, Canada},
  month     = dec
}

@inproceedings{ISSPIT09_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Random Hypersurface Models for Extended Object Tracking}},
  booktitle = {{Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009)}},
  year      = {2009},
  address   = {Ajman, United Arab Emirates},
  month     = dec,
  abstract  = {Target tracking algorithms usually assume that the received measurements
               stem from a point source. However, in many scenarios  this assumption
               is not feasible so that measurements may stem from different locations,
               named measurement sources, on the target surface. Then, it is necessary
               to incorporate the target extent into the estimation procedure in
               order to obtain robust and precise estimation results. This paper
               introduces the novel concept of Random Hypersurface Models for extended
               targets. A Random Hypersurface Model assumes that each measurement
               source is an element of a randomly generated hypersurface. The applicability
               of this approach is demonstrated by means of an elliptic target shape.
               In this case, a Random Hypersurface Model specifies the random (relative)
               Mahalanobis distance of a measurement source to the center of the
               target object. As a consequence, good estimation results can be obtained
               even if the true target shape significantly differs from the modeled
               shape. Additionally, Random Hypersurface Models are computationally
               tractable with standard nonlinear stochastic state estimators.},
  pdf       = {ISSPIT09_Baum.pdf},
  timestamp = {2009.10.13}
}

@inproceedings{Deisenroth2009,
  author    = {Marc P. Deisenroth and Carl E. Rasmussen},
  title     = {{Efficient Reinforcement Learning for Motor Control}},
  booktitle = {Proceedings of the 10th International PhD Workshop on Systems and Control},
  address   = {Hluboka nad Vltavou, Czech Republic},
  year      = {2009},
  month     = sep
}

@inproceedings{CDC09_HanebeckHuber,
  author    = {Uwe D. Hanebeck and Marco F. Huber and Vesa Klumpp},
  title     = {{Dirac Mixture Approximation of Multivariate Gaussian Densities}},
  booktitle = {Proceedings of the 2009 IEEE Conference on Decision and Control (CDC 2009)},
  year      = {2009},
  address   = {Shanghai, China},
  pdf       = {CDC09_Hanebeck.pdf},
  abstract  = {For the optimal approximation of multivariate
               Gaussian densities by means of Dirac mixtures, i.e., by means of
               a sum of weighted Dirac distributions on a continuous domain,
               a novel systematic method is introduced. The parameters of
               this approximate density are calculated by minimizing a global
               distance measure, a generalization of the well-known Cram\'{e}r-
               von Mises distance to the multivariate case. This generalization
               is obtained by defining an alternative to the classical cumulative
               distribution, the Localized Cumulative Distribution (LCD). In
               contrast to the cumulative distribution, the LCD is unique
               and symmetric even in the multivariate case. The resulting
               deterministic approximation of Gaussian densities by means of
               discrete samples provides the basis for new types of Gaussian
               filters for estimating the state of nonlinear dynamic systems
               from noisy measurements.},
  month     = dec
}

@inproceedings{IROS09_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck and G{\'{a}}bor Szab{\'{o}}},
  title     = {{Heart Surface Motion Estimation Framework for Robotic Surgery Employing Meshless Methods}},
  booktitle = {{Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009)}},
  month     = oct,
  pdf       = {IROS09_Bogatyrenko.pdf},
  abstract  = {A novel heart surface motion estimation frame-
               work for a robotic surgery on a stabilized beating heart is
               proposed. It includes an approach for the reconstruction and
               prediction of heart surface motion based on a novel physical
               model of the intervention area described by a distributed-
               parameter system. Instead of conventional element methods, a
               meshless method is used for a spatial and temporal decomposi-
               tion of this system. This leads to a finite-dimensional state-space
               form. Furthermore, the state of the resulting lumped-parameter
               system, which provides an approximation of the deflection and
               velocity of the heart surface, is dynamically estimated under
               consideration of uncertainties both occurring in the system
               and arising from noisy camera measurements. By using the
               estimation results, an accurate reconstruction of heart surface
               motion for the synchronisation of the surgical instruments is
               also achieved at occluded or non-measurement points.},
  year      = {2009}
}

@inproceedings{SDF09_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking an Extended Object Modeled as an Axis-Aligned Rectangle}},
  booktitle = {{4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft f{\"u}r Informatik e.V. (GI)}},
  year      = {2009},
  address   = {L{\"u}beck, Germany},
  pdf       = {SDF09_Baum.pdf},
  abstract  = {In many tracking applications, the extent of the target object is neglected
               and it is assumed that the received measurements stem from a point source. However,
               modern sensors are able to supply several measurements from different scattering cen-
               ters on the target object due to their high-resolution capability. As a consequence, it
               becomes necessary to incorporate the target extent into the estimation procedure. This
               paper introduces a new method for tracking the smallest enclosing rectangle of an ex-
               tended object with an unknown shape. At each time step, a finite set of noisy position
               measurements that stem from arbitrary, unknown measurement sources on the target
               surface may be available. In contrast to common approaches, the presented approach
               does not have to make any statistical assumptions on the measurement sources.},
  month     = oct
}

@inproceedings{CCA09_RauhHanebeck,
  author    = {Andreas Rauh and Kai Briechle and Uwe D. Hanebeck},
  title     = {{Nonlinear Measurement Update and Prediction: Prior Density Splitting Mixture Estimator}},
  booktitle = {Proceedings of the 2009 IEEE International Conference on Control Applications (CCA 2009)},
  month     = jul,
  pdf       = {CCA09_RauhHanebeck.pdf},
  year      = {2009},
  abstract  = {In this paper, the Prior Density Splitting Mixture
               Estimator (PDSME), a new Gaussian mixture filtering
               algorithm for nonlinear dynamical systems and nonlinear
               measurement equations, is introduced. This filter reduces the
               linearization error which typically arises if nonlinear state and
               measurement equations are linearized to apply linear filtering
               techniques. For that purpose, the PDSME splits the prior probability
               density into several components of a Gaussian mixture
               with smaller covariances. The PDSME is applicable to both
               prediction and filter steps. A measure for the linearization error
               similar to the Kullback-Leibler distance is introduced allowing
               the user to specify the desired estimation quality. An upper
               bound for the computational effort can be given by limiting
               the maximum number of Gaussian mixture components.}
}

@inproceedings{Fusion09_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Baum.pdf},
  abstract  = {In this paper, a novel approach for tracking
               extended objects is presented. The target object is
               modeled as a circular disc such that the center and
               extent of the target object can be estimated. At each
               time step, a finite set of position measurements that
               are corrupted with stochastic noise may be available.
               Each position measurement stems from an unknown measurement
               source on the extended object. In contrast to existing
               approaches, no statistical assumptions about the distribution
               of the measurement sources on the extended object are made.
               As a consequence, it is necessary to deal with stochastic
               and set-valued uncertainties. For this purpose, a novel
               combined stochastic and set-theoretic estimator that employs
               random hyperboloids to express the uncertainties about the
               true circular disc is derived.},
  timestamp = {2009.04.21}
}

@inproceedings{Fusion09_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Gaussian Filtering using State Decomposition Methods}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Beutler.pdf},
  abstract  = {State estimation for nonlinear systems generally
               requires approximations of the system or the probability
               densities, as the occurring prediction and filtering equations
               cannot be solved in closed form. For instance, Linear Regression
               Kalman Filters like the Unscented Kalman Filter
               or the considered Gaussian Filter propagate a small set of
               sample points through the system to approximate the posterior
               mean and covariance matrix. To reduce the number of
               sample points, special structures of the system and measurement
               equation can be taken into account. In this paper, two
               principles of system decomposition are considered and applied
               to the Gaussian Filter. One principle exploits that only
               a part of the state vector is directly observed by the measurement.
               The second principle separates the system equations
               into linear and nonlinear parts in order to merely approximate
               the nonlinear part of the state. The benefits of both
               decompositions are demonstrated on a real-world example.},
  timestamp = {2009.04.20}
}

@inproceedings{Fusion09_Hoerst-SGMF,
  author    = {Julian H{\"o}rst and Felix Sawo and Vesa Klumpp and Uwe D. Hanebeck and Dietrich Fr{\"a}nken},
  title     = {{Extension of the Sliced Gaussian Mixture Filter with Application to Cooperative Passive Target Tracking}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Hoerst.pdf},
  abstract  = {This paper copes with the problem of nonlinear Bayesian state estimation.
               A nonlinear filter, the Sliced Gaussian Mixture Filter (SGMF), employs linear
               substructures in the nonlinear measurement and prediction model in order to
               simplify the estimation process.
               Here, a special density representation, the sliced Gaussian mixture
               density, is used to derive an exact solution of the Chapman-Kolmogorov equation.
               The sliced Gaussian mixture density is obtained by a systematic and deterministic
               approximation of a continuous density minimizing a certain distance measure.
               In contrast to previous work, improvements of the SGMF presented here include an
               extended system model and the processing of multi-dimensional nonlinear
               subspaces. As an application for the SGMF, cooperative passive target tracking,
               where sensors take angular measurements from a target, is considered in this paper.
               Finally, the performance of the proposed estimator is compared to the
               marginalized particle filter (MPF) in simulations.},
  timestamp = {2009.04.21}
}

@inproceedings{Fusion09_Huber,
  author    = {Marco F. Huber and Achim Kuwertz and Felix Sawo and Uwe D. Hanebeck},
  title     = {{Distributed Greedy Sensor Scheduling for Model-based Reconstruction of Space-Time Continuous Physical Phenomena}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_HuberKuwertz.pdf},
  abstract  = {A novel distributed sensor scheduling method for large-scale sensor
               networks observing space-time continuous physical phenomena is
               introduced. In a first step, the model of the distributed phenomenon
               is spatially and temporally decomposed leading to a linear
               probabilistic finite-dimensional model. Based on this representation,
               the information gain of sensor measurements is evaluated by means of
               the so-called covariance reduction function. For this reward function,
               it is shown that the performance of the greedy sensor scheduling is at
               least half that of the optimal scheduling considering long-term
               effects. This finding is the key for distributed sensor scheduling,
               where a central processing unit or fusion center is unnecessary, and
               thus, scaling as well as reliability is ensured. Hence, greedy
               scheduling in combination with a proposed hierarchical communication
               scheme requires only local sensor information and communication.}
}

@inproceedings{Fusion09_Klumpp-FusionND,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Nonlinear Fusion of Multi-Dimensional Densities in Joint State Space}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  timestamp = {2009.04.21},
  pdf       = {Fusion09_Klumpp-FusionND.pdf},
  abstract  = {Nonlinear fusion of multi-dimensional densities
               is an important application in Bayesian state estimation.
               In the approach proposed here, a joint density over all
               considered densities is build, which is then approximated
               by means of a Dirac mixture density by partitioning the
               joint state space into regions that are represented by single
               Dirac components. This approximation procedure depends
               on the nonlinear fusion model and only areas relevant to this
               model are considered. The processing in joint state space
               has advantages, especially when fusing Dirac mixture densities.
               Within this approach, degeneration can be avoided
               and even densities without mutual support can be combined.
               Thus, this approach gives an alternative to multiplication of
               Dirac mixtures with a likelihood, as used in the particle filter.
               Furthermore, a nonlinear Bayesian estimator with filter
               and prediction step can be formulated, which is able to cope
               with both discrete and continuous densities.}
}

@inproceedings{Fusion09_Klumpp-Type2Dens,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Bayesian Estimation with Uncertain Parameters of Probability Density Functions}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Klumpp-Type2Dens.pdf},
  abstract  = {In this paper, we address the problem of
               processing imprecisely known probability density func-
               tions by means of Bayesian estimation. The imprecise
               knowledge about probability density functions is given
               as stochastic uncertainty about their parameters. The
               proposed processing of this special density in a Bayesian
               estimator is accomplished by reinterpretation of the Fil-
               ter and prediction equations. Here, the parameters are
               treated as a higher order state, which can be processed
               by Bayesian estimation techniques. For state estima-
               tion, this avoids the need to select specific values for
               unknown parameters and, thus, allows the processing of
               all potential parameters at once. The proposed approach
               further allows the use of imprecisely known model equa-
               tions for measurement and state prediction by the same
               principle.}
}

@inproceedings{Fusion09_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Krauthausen.pdf},
  abstract  = {In this paper, a novel probabilistic ap-
               proach to intention recognition for partial-order plans
               is proposed. The key idea is to exploit independences
               between subplans to substantially reduce the state space
               sizes in the compiled Dynamic Bayesian Networks.
               This makes inference more efficient. The main con-
               tributions are the computationally exploitable definition
               of subplan structures, the introduction of a novel Lay-
               ered Intention Model and a Dynamic Bayesian Net-
               work representation with an inference mechanism that
               exploits consecutive and concurrent subplans' indepen-
               dences. The presented approach reduces the state space
               to the order of the most complex subplan and requires
               only minor changes in the standard inference mecha-
               nism. The practicability of this approach is demon-
               strated by recognizing the process of shelf-assembly.}
}

@inproceedings{Fusion09_Noack,
  author    = {Benjamin Noack and Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{State Estimation with Sets of Densities considering Stochastic and Systematic Errors}},
  booktitle = {Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)},
  address   = {Seattle, Washington, USA},
  year      = {2009},
  month     = jul,
  pdf       = {Fusion09_Noack.pdf},
  abstract  = {In practical applications, state estimation requires the consideration of
               stochastic and systematic errors. If both error types are present, an exact
               probabilistic description of the state estimate is not possible, so that
               common Bayesian estimators have to be questioned. This paper introduces a
               theoretical concept, which allows for incorporating unknown but bounded errors
               into a Bayesian inference scheme by utilizing sets of densities. In order to
               derive a tractable estimator, the Kalman filter is applied to ellipsoidal sets
               of means, which are used to bound additive systematic errors. Also, an
               extension to nonlinear system and observation models with ellipsoidal error
               bounds is presented. The derived estimator is motivated by means of two
               example applications.}
}

@inproceedings{Fusion09_Schieferdecker,
  author    = {Dennis Schieferdecker and Marco F. Huber},
  title     = {{Gaussian Mixture Reduction via Clustering}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_SchieferdeckerHuber.pdf},
  abstract  = {Recursive processing of Gaussian mixture functions inevitably leads to a
               large number of mixture components. In order to keep the computational
               complexity at a feasible level, the number of their components has to be
               reduced periodically. There already exists a variety of algorithms for
               this purpose, bottom-up and top-down approaches, methods that take the
               global structure of the mixture into account or that work locally and
               consider few mixture components at the same time. The mixture reduction
               algorithm presented in this paper can be categorized as global top-down
               approach. It takes a clustering algorithm originating from the field of
               theoretical computer science and adapts it for the problem of Gaussian
               mixture reduction. The achieved results are on the same scale as the
               results of the current "state-of-the-art" algorithm PGMR, but, depending
               on the input size, the whole procedure performs significantly faster.},
  timestamp = {2009.04.20}
}

@inproceedings{MSRL09_Deisenroth,
  author    = {Marc Peter Deisenroth and Carl Edward Rasmussen},
  title     = {{Bayesian Inference for Efficient Learning in Control}},
  booktitle = {{Multidisciplinary Symposium on Reinforcement Learning (MSRL)}},
  year      = {2009},
  address   = {Montreal, Canada},
  month     = jun,
  pdf       = {PHDWS09_Deisenroth.pdf}
}

@inproceedings{ICINCO09_Perez,
  author    = {Antonia P\'{e}rez Arias and Uwe D. Hanebeck},
  title     = {{A Novel Haptic Interface for Extended Range Telepresence:
               Control and Evaluation}},
  booktitle = {Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009)},
  year      = {2009},
  pages     = {222--227},
  address   = {Milan, Italy},
  month     = jul,
  pdf       = {ICINCO09_Perez.pdf},
  abstract  = {A novel haptic interface for extended range telepresence
               is presented that allows the user simultaneous wide area motion and
               haptic interaction in remote environments. To achieve an extended
               workspace, the haptic interface consists of a haptic manipulator for
               precise haptic rendering and a large portal carrier system that
               enlarges the workspace by prepositioning the end-effector. As the
               prepositioning unit is grounded and driven by three linear drives, our
               approach has the advantages of high force capability, and an accurate
               positioning of the haptic interface. The use of this haptic interface
               with Motion Compression permits to explore large remote environments
               even from small user environments. As a result, not only has the user
               visual, acoustic, and haptic feedback but can also control the
               teleoperator or avatar by natural walking, which considerably increases
               the sense of immersion. A prototype system for haptic extended range
               telepresence was designed, implemented, and tested.},
  keywords  = {Extended range telepresence, motion compression, haptic interface, force control}
}

@inproceedings{ICML09_Deisenroth,
  author    = {Marc P. Deisenroth and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Analytic Moment-based Gaussian Process Filtering}},
  booktitle = {{26th International Conference on Machine Learning (ICML 2009) in Montreal, Canada}},
  month     = jun,
  year      = {2009},
  abstract  = {We propose an analytic moment-based filter for nonlinear stochastic
               dynamic systems modeled by Gaussian processes. Exact expressions for the
               expected value and the covariance matrix are provided for both the
               prediction step and the filter step, where an additional Gaussian
               assumption is exploited in the latter case. Our filter does not require
               further approximations. In particular, it avoids finite-sample
               approximations. We compare the filter to a variety of Gaussian filters,
               that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al.
               (2007).},
  pdf       = {ICML09_DeisenrothHuber.pdf}
}

@inproceedings{CASA09_Perez,
  author    = {Antonia P\'{e}rez Arias and Tobias Kretz and Peter Ehrhardt and Stefan Hengst and Peter Vortisch and Uwe D. Hanebeck},
  title     = {{A Framework for Evaluating the VISSIM Traffic Simulation with Extended Range Telepresence}},
  booktitle = {Proceedings of the 22nd Annual Conference on Computer Animation and Social Agents (CASA 2009)},
  year      = {2009},
  address   = {Amsterdam, The Netherlands},
  month     = jun,
  pages     = {13--16},
  pdf       = {CASA09_Perez.pdf},
  abstract  = {This paper presents a novel framework
               for combining traffic simulations and extended range
               telepresence. The real user's position data can thus
               be used for validation and calibration of models of
               pedestrian dynamics, while the user experiences a
               high degree of immersion by interacting with agents
               in realistic simulations.}
}

@inproceedings{ICASSP09_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Instantaneous Pose Estimation using Rotation Vectors}},
  booktitle = {{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan}},
  month     = apr,
  year      = {2009},
  pdf       = {ICASSP09_Beutler.pdf},
  abstract  = {An algorithm for estimating the pose, i.e., translation and rotation, of
               an extended target object is introduced. Compared to conventional
               methods, where pose estimation is performed on the basis of timeof-
               flight (TOF) measurements between external sources and sensors
               attached to the object, the proposed approach directly uses the amplitude
               values measured at the sensors for estimation purposes without
               an intermediate TOF estimation step. This is achieved by modeling
               the wave propagation by a nonlinear dynamic system comprising a
               system and a measurement equation. The nonlinear system equation
               includes a model of the time-variant structure of the object rotation
               based on rotation vectors. As a result, the measured amplitude values
               at the sensors can be processed instantaneously in a recursive
               fashion. Uncertainties in the measurement process are systematically
               considered by employing a stochastic filter for estimating the
               pose, i.e., the state of the nonlinear dynamic system.},
  pages     = {3413--3416}
}

@article{MFI09_Beutler,
  author   = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title    = {Probabilistic Instantaneous Model-Based Signal Processing applied to Localization and Tracking},
  journal  = {{Journal of Robotics and Autonomous Systems, Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006)}},
  month    = mar,
  year     = {2009},
  volume   = {57},
  number   = {3},
  pages    = {249--258},
  abstract = {In this paper, a probabilistic approach for estimating time
              and space-variant parameters of a system, based on sequentially received
              discrete-time signal values, is presented. The system description is the
              solution of a linear partial differential equation (PDE). The PDE describes
              for example the wave propagation of an acoustic wave in a localization
              system. The solution of the PDE is given by a time-variant and space-variant
              impulse response. This impulse response is characterized by the time and
              space-variant parameters in order to track an object, which emits for example
              an acoustic signal. For estimating the position of the object in an
              instantaneous way a Bayesian approach has to be used, which considers the
              dynamic behavior of the parameters in a system model and uncertainties in a
              stochastic manner by means of probability density functions. Hence, the new
              approach provides a probabilistic instantaneous model-based signal processing,
              where the sequentially measured signal values are processed directly and known
              reference signal sequences are interpreted as part of a time-variant nonlinear
              measurement equation.},
  url      = {https://dx.doi.org/10.1016/j.robot.2008.10.013},
  note     = {doi:10.1016/j.robot.2008.10.013}
}

@book{MFI09_Hanebeck,
  title     = {Robotics and Autonomous Systems},
  publisher = {ScienceDirect},
  volume    = {57},
  number    = {3},
  pages     = {237--344},
  year      = {2009},
  editor    = {Uwe D. Hanebeck and Thomas C. Henderson},
  series    = {Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006), 2006 IEEE International Conference on Multisensor Fusion and Integration},
  month     = sep,
  pdf       = {MFI09_Hanebeck.pdf}
}

@article{NC09_Deisenroth,
  author    = {Marc P. Deisenroth and Carl E. Rasmussen and Jan Peters},
  title     = {Gaussian {Process Dynamic Programming}},
  journal   = {Neurocomputing},
  year      = {2009},
  volume    = {72},
  pages     = {1508--1524},
  number    = {7--9},
  month     = mar,
  abstract  = {Reinforcement learning (RL) and optimal control of systems with continuous
               states and actions require approximation techniques in most interesting	cases.
               In this article, we introduce Gaussian process dynamic programming '(GPDP), an
               approximate value-function based RL algorithm. We consider both a classic optimal
               control problem, where problem-specific prior knowledge is available,
               and a classic RL problem, where only very general priors can be used.
               For the classic optimal control problem, GPDP models the unknown value
               functions with Gaussian processes and generalizes dynamic programming to continuous-valued
               states and actions. For the RL problem, GPDP starts from a given initial state
               and explores the state space using Bayesian active learning. To
               design a fast learner, available data has to be used efficiently.
               Hence, we propose to learn probabilistic models of the a priori unknown
               transition dynamics and the value functions on the fly. In both
               cases, we successfully apply the resulting continuous-valued controllers
               to the under-actuated pendulum swing up and analyze the performances of the
               suggested algorithms. It turns out that GPDP uses data very efficiently and
               can be applied to problems, where classic dynamic programming would be cumbersome.},
  doi       = {10.1016/j.neucom.2008.12.019},
  timestamp = {2008.06.16},
  pdf       = {Neurocomputing09_DeisenrothRasmussenPeters_preprint.pdf}
}

@incollection{LNEE_ICINCO08_Schrempf,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{Dirac Mixture Approximation for Nonlinear Stochastic Filtering}},
  booktitle = {Informatics in Control, Automation and Robotics --- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007},
  publisher = {Springer},
  year      = {2008},
  series    = {Lecture Notes in Electrical Engineering},
  volume    = {24},
  month     = sep,
  pages     = {287--300},
  timestamp = {2007.12.19},
  abstract  = {This work presents a filter for estimating the state of
               nonlinear dynamic systems. It is based on optimal recursive approximation
               the state densities by means of Dirac mixture functions in order to allow
               for a closed form solution of the prediction and filter step. The
               approximation approach is based on a systematic minimization of a distance
               measure and is hence optimal and deterministic. In contrast to non-deterministic
               methods we are able to determine the optimal number of components in the Dirac
               mixture. A further benefit of the proposed approach is the consideration of
               measurements during the approximation process in order to avoid parameter degradation.},
  url       = {https://link.springer.com/chapter/10.1007/978-3-540-85640-5_22}
}

@incollection{LNEE_ICINCO08_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations}},
  booktitle = {Informatics in Control, Automation and Robotics --- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007},
  publisher = {Springer},
  year      = {2008},
  series    = {Lecture Notes in Electrical Engineering},
  volume    = {24},
  month     = sep,
  pages     = {239--252},
  timestamp = {2007.12.19},
  abstract  = {In this paper, a framework for stochastic Nonlinear
               Model Predictive Control (NMPC) that explicitly incorporates the
               noise influence on systems with continuous state spaces is introduced.
               By the incorporation of noise, which results from uncertainties during
               model identification and measurement, the quality of control can be
               significantly increased. Since stochastic NMPC requires the prediction
               of system states over a certain horizon, an efficient state prediction
               technique for nonlinear noise-affected systems is required. This is
               achieved by using transition densities approximated by axis-aligned
               Gaussian mixtures together with methods to reduce the computational burden.
               A versatile cost function representation also employing Gaussianmixtures
               provides an increased freedom of modeling. Combining the rediction technique
               with this value function representation allows closed-form calculation of
               the necessary optimization problems arising from stochastic NMPC. The
               capabilities of the framework and especially the benefits that can be
               gained by considering the noise in the controller are illustrated by the
               example of a mobile robot following a given path.},
  url       = {https://link.springer.com/chapter/10.1007/978-3-540-85640-5_18}
}

@inproceedings{MFI08_Hanebeck-LCD,
  author    = {Uwe D. Hanebeck and Vesa Klumpp},
  title     = {{Localized Cumulative Distributions and a Multivariate Generalization of the Cram\'{e}r-von Mises Distance}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_Hanebeck-LCD.pdf},
  pages     = {33--39},
  abstract  = {This paper is concerned with distances for comparing
               multivariate random vectors with a special focus on the case
               that at least one of the random vectors is of discrete type, i.e.,
               assumes values from a discrete set only. The first contribution
               is a new type of characterization of multivariate random
               quantities, the so called Localized Cumulative Distribution
               (LCD) that, in contrast to the conventional definition of a
               cumulative distribution, is unique and symmetric. Based on the
               LCDs of the random vectors under consideration, the second
               contribution is the definition of generalized distance measures
               that are suitable for the multivariate case. These distances
               are used for both analysis and synthesis purposes. Analysis
               is concerned with assessing whether a given sample stems from
               a given continuous distribution. Synthesis is concerned with
               both density estimation, i.e., calculating a suitable continuous
               approximation of a given sample, and density discretization,
               i.e., approximation of a given continuous random vector by a
               discrete one.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_HuberBailey,
  author    = {Marco F. Huber and Tim Bailey and Hugh Durrant-Whyte and Uwe D. Hanebeck},
  title     = {{On Entropy Approximation for Gaussian Mixture Random Vectors}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_HuberBailey.pdf},
  pages     = {181--188},
  abstract  = {For many practical probability density representations
               such as for the widely used Gaussian mixture densities, an
               analytic evaluation of the differential entropy is not possible and
               thus, approximate calculations are inevitable. For this purpose,
               the first contribution of this paper deals with a novel entropy
               approximation method for Gaussian mixture random vectors,
               which is based on a component-wise Taylor-series expansion of
               the logarithm of a Gaussian mixture and on a splitting method
               of Gaussian mixture components. The employed order of the
               Taylor-series expansion and the number of components used for
               splitting allows balancing between accuracy and computational
               demand. The second contribution is the determination of meaningful
               and efficiently to calculate lower and upper bounds of the
               entropy, which can be also used for approximation purposes.
               In addition, a refinement method for the more important upper
               bound is proposed in order to approach the true entropy value.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_Klumpp-DiracFusion,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Direct Fusion of Dirac Mixture Densities using an Efficient Approximation in Joint State Space}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_Klumpp-DiracFusion.pdf},
  pages     = {168--174},
  abstract  = {In this paper, we present a direct fusion algorithm
               for processing the combination of two Dirac mixture densities.
               The proposed approach allows the multiplication of two Dirac
               mixture densities without requiring identical support and thus
               enables the fusion of two independently generated sample sets.
               The resulting posterior Dirac mixture density is an approximation
               of the true continuous density that would result from the
               processing of the underlying true continuous density functions.
               This procedure is based on a suboptimal greedy approximation
               of the joint state space by means of a Dirac mixture that
               iteratively increases the resolution of the fusion result while
               considering only the relevant regions in the joint state space,
               where the fusion constraint holds.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_Klumpp-DiracTrees,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Dirac Mixture Trees for Fast Suboptimal Multi-Dimensional Density Approximation}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_Klumpp-DiracTrees.pdf},
  pages     = {593--600},
  abstract  = {We consider the problem of approximating an
               arbitrary multi-dimensional probability density function by
               means of a Dirac mixture density. Instead of an optimal
               solution based on minimizing a global distance measure between
               the true density and its approximation, a fast suboptimal
               anytime procedure is proposed, which is based on sequentially
               partitioning the state space and component placement by local
               optimization. The proposed procedure adaptively covers the
               entire state space with a gradually increasing resolution. It
               can be efficiently implemented by means of a pre-allocated
               tree structure in a straightforward manner. The resulting computational
               complexity is linear in the number of components
               and linear in the number of dimensions. This allows a large
               number of components to be handled, which is especially useful
               in high-dimensional state spaces.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_NaWang,
  author    = {Chongning Na and Hui Wang and Dragan Obradovic and Uwe D. Hanebeck},
  title     = {{Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  pages     = {290--295},
  abstract  = {Many distributed inference problems in wireless
               sensor networks can be represented by probabilistic graphical
               models, where belief propagation, an iterative message passing
               algorithm provides a promising solution. In order to make the
               algorithm efficient and accurate, messages which carry the
               belief information from one node to the others should be
               formulated in an appropriate format. This paper presents two
               belief propagation algorithms where non-linear and
               non-Gaussian beliefs are approximated by Fourier density
               approximations, which significantly reduces power
               consumptions in the belief computation and transmission. We
               use self-localization in wireless sensor networks as an example to
               illustrate the performance of this method.},
  url       = {https://dx.doi.org/10.1109/MFI.2008.4648080},
  pdf       = {MFI08_NaWang.pdf},
  month     = aug
}

@inproceedings{MFI08_Sawo-Localization,
  author    = {Felix Sawo and Thomas C. Henderson and Christopher Sikorski and Uwe D. Hanebeck},
  title     = {{Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  timestamp = {2008.06.27},
  pdf       = {MFI08_Sawo-Localization.pdf},
  pages     = {301--308},
  abstract  = {This paper addresses the model-based localization
               of sensor networks based on local observations of a distributed
               phenomenon. For the localization process, we propose the
               rigorous exploitation of strong mathematical models of distributed
               phenomena. By unobtrusively exploiting background
               phenomena, the individual sensor nodes can be localized by
               only observing its local surrounding without the necessity of
               heavy infrastructure. In this paper, we introduce two novel
               approaches: (a) the polynomial system localization method
               (PSL-method) and (b) the simultaneous reconstruction and
               localization method (SRL-method). The first approach (PSLmethod)
               is based on restating the mathematical model of the
               distributed phenomenon in terms of a polynomial system.
               These equations depend on both the state of the phenomenon
               and the node locations. Solving the system of polynomials
               for each individual sensor node directly leads to the desired
               locations. The second approach (SRL-method) basically regards
               the localization problem as a simultaneous state and parameter
               estimation problem with the node locations as parameters. By
               this means, the distributed phenomenon is reconstructed and
               the individual nodes are localized in a simultaneous fashion.
               In addition, within this framework the uncertainties in the
               mathematical model and the measurements are considered.
               The performance of the two different localization approaches
               is demonstrated by means of simulation results.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/MFI08_BestPaperAward_Cerificate.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{MFI08_Schwarzenberg,
  author    = {Gregor F. Schwarzenberg and Uwe Mayer and Nicole V. Ruiter and Uwe D. Hanebeck},
  title     = {{3D Reflectivity Reconstruction by Means of Spatially Distributed Kalman Filters}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pages     = {384--391},
  abstract  = {In seismic, radar, and sonar imaging the exact determination
               of the reflectivity distribution is usually intractable
               so that approximations have to be applied. A method called
               synthetic aperture focusing technique (SAFT) is typically used
               for such applications as it provides a fast and simple method to
               reconstruct (3D) images. Nevertheless, this approach has several
               drawbacks such as causing image artifacts as well as offering
               no possibility to model system-specific uncertainties.
               In this paper, a statistical approach is derived, which models
               the region of interest as a probability density function (PDF)
               representing spatial reflectivity occurrences. To process the
               nonlinear measurements, the exact PDF is approximated by
               well-placed Extended Kalman Filters allowing for efficient and
               robust data processing.
               The performance of the proposed method is demonstrated
               for a 3D ultrasound computer tomograph and comparisons are
               carried out with the SAFT image reconstruction.},
  pdf       = {MFI08_Schwarzenberg.pdf}
}

@inproceedings{MFI08_WangSzabo,
  author    = {Hui Wang and Andrei Szabo and Joachim Bamberger and Uwe D. Hanebeck},
  title     = {{Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  pages     = {445--450},
  abstract  = {Typical WLAN based indoor positioning systems
               take the received signal strength (RSS) as the major information
               source. Due to the complicated indoor environment, the RSS
               measurements are hard to model and too noisy to achieve a
               satisfactory 3-D accuracy in multi-floor scenarios. To enhance
               the performance of WLAN positioning systems, extra information
               sources could be integrated. In this paper, a Bayesian
               framework is applied to fuse multi-information sources and
               estimate the spatial and time varying parameters simultaneously
               and adaptively. An application of this framework, which
               fuses pressure measurements, a topological building map with
               RSS measurements, and simultaneously estimates the pressure
               sensor bias, is investigated. Our experiments indicate that the
               localization performance is more accurate and robust by using
               our approach.},
  url       = {https://dx.doi.org/10.1109/MFI.2008.4648035},
  pdf       = {MFI08_WangSzabo.pdf},
  month     = aug
}

@inproceedings{MFI08_WeisselSchreiter,
  author    = {Florian Weissel and Thomas Schreiter and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Stochastic Model Predictive Control of Time-Variant Nonlinear Systems with Imperfect State Information}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_WeisselSchreiter.pdf},
  pages     = {40--46},
  abstract  = {In many technical systems, the system state, which
               is to be controlled, is not directly accessible, but has to be
               estimated from observations. Furthermore, the uncertainties
               arising from this procedure are typically neglected in the
               controller. To remedy this deficiency, in this paper, we present a
               novel approach to stochastic nonlinear model predictive control
               (NMPC) for heavily noise-affected systems with not directly
               accessible, i.e., hidden states, extending the stochastic NMPCframework
               presented in [1]. An important property of our novel
               method is that, in contrast to classical approaches, time-variant
               system and measurement equations as well as time-variant step
               rewards can be considered. Extending the techniques from
               [1] by introducing virtual future observations and combining
               this with a novel tree search algorithm, called probabilistic
               branch-and-bound search (PBAB), a solution with a feasible
               computational demand of the challenging problem is possible.}
}

@inproceedings{IFAC08_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation}},
  booktitle = {Proceedings of the 17th IFAC World Congress (IFAC 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = jul,
  pdf       = {IFAC08_Huber.pdf},
  volume    = {17},
  number    = {2},
  abstract  = {In this paper, a Gaussian filter for nonlinear Bayesian estimation is introduced that is
               based on a deterministic sample selection scheme. For an effective sample selection, a parametric
               density function representation of the sample points is employed, which allows approximating the
               cumulative distribution function of the prior Gaussian density. The computationally demanding
               parts of the optimization problem formulated for approximation are carried out off-line for
               obtaining an efficient filter, whose estimation quality can be altered by adjusting the number
               of used sample points. The improved performance of the proposed Gaussian filter compared to
               the well-known unscented Kalman fiter is demonstrated by means of two examples.}
}

@inproceedings{IFAC08_SawoBeutler,
  author    = {Felix Sawo and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Decentralized State Estimation of Distributed Phenomena based on Covariance Bounds}},
  booktitle = {Proceedings of the 17th IFAC World Congress (IFAC 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = jul,
  pdf       = {IFAC08_SawoBeutler.pdf},
  volume    = {17},
  number    = {2},
  abstract  = {This paper addresses the problem of decentralized state estimation of distributed physical
               phenomena observed by a sensor network. The centralized approaches are not scalable for large
               sensor networks, because all information has to be transmitted to a powerful central processing node
               requiring an extensive amount of communication bandwidth and a lot of processing power. Thus, for a
               decentralized reconstruction of distributed phenomena, we propose a novel methodology consisting of
               three steps: (a) conversion of the distributed phenomenon into a lumped-parameter system description,
               (b) decomposition of the resulting system in order to map the description to the actual sensor network,
               and (c) decomposition of the density representation leading to a decentralized estimation approach. The
               main problem of a decentralized approach is that due to the propagation of local information through the
               network, unknown correlations are caused. This fact needs to be considered during the reconstruction
               process in order to get correct and consistent estimation results. For that reason, we employ a robust
               estimator (based on Covariance Bounds) for the local reconstruction update on each sensor node. By this
               means, the individual sensor nodes are able to estimate the local state of the distributed phenomenon
               using local estimates obtained and communicated by adjacent nodes only. The information about their
               correlations is not stored in the sensor network.}
}

@inproceedings{IFAC08_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Stochastic Optimal Control based on Value-Function Approximation using Sinc Interpolation}},
  booktitle = {Proceedings of the 17th IFAC World Congress (IFAC 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = jul,
  pdf       = {IFAC08_WeisselHuber.pdf},
  volume    = {17},
  number    = {2},
  abstract  = {An effcient approach for solving stochastic optimal control problems is to employ
               dynamic programming (DP). For continuous-valued nonlinear systems, the corresponding
               DP recursion generally cannot be solved in closed form. Thus, a typical approach is to
               discretize the DP value functions in order to be able to carry out the calculation. Especially
               for multidimensional systems, either a large number of discretization points is necessary or
               the quality of approximation degrades. This problem can be alleviated by interpolating the
               discretized value function. In this paper, we present an approach based on optimal low-pass
               interpolation employing sinc functions (sine cardinal). For the important case of systems with
               Gaussian mixture noise (including the special case of Gaussian noise), we show how the
               calculations required for this approach, especially the nontrivial calculation of an expected
               value of a Gaussian mixture random variable transformed by a sinc function, can be carried out
               analytically. We illustrate the effectiveness of the proposed interpolation scheme by an example
               from the field of Stochastic Nonlinear Model Predictive Control (SNMPC).}
}

@inproceedings{Fusion08_Huber-PGMR,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Progressive Gaussian Mixture Reduction}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_Huber-PGMR.pdf},
  pages     = {1--8},
  abstract  = {For estimation and fusion tasks it is inevitable to
               approximate a Gaussian mixture by one with fewer components
               to keep the complexity bounded. Appropriate approximations
               can be typically generated by exploiting the redundancy in
               the shape description of the original mixture. In contrast to
               the common approach of successively merging pairs of components
               to maintain a desired complexity, the novel Gaussian
               mixture reduction algorithm introduced in this paper avoids
               to directly reduce the original Gaussian mixture. Instead, an
               approximate mixture is generated from scratch by employing
               homotopy continuation. This allows starting the approximation
               with a single Gaussian, which is constantly adapted to the
               progressively incorporated true Gaussian mixture. Whenever a
               user-defined bound on the deviation of the approximation cannot
               be maintained during the continuation, further components are
               added to the approximation. This facilitates significantly reducing
               the number of components even for complex Gaussian mixtures.}
}

@inproceedings{Fusion08_Huber_PLSS,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Priority List Sensor Scheduling using Optimal Pruning}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_Huber_PLSS.pdf},
  pages     = {1--8},
  abstract  = {State estimation and reconstruction quality of distributed
               phenomena that are monitored by a network of distributed
               sensors is strongly affected by communication failures,
               which is a problem in real-world sensor networks. In this paper,
               we propose a novel sensor scheduling approach named priority
               list sensor scheduling (PLSS). This approach facilitates efficient
               distributed estimation in sensor networks, even in case of unreliable
               communication, by prioritizing the sensor nodes according
               to local sensor schedules based on the predicted estimation
               error. It is shown that PLSS minimizes the expected estimation
               error for arbitrary packet-loss or transmission probabilities.
               As prioritizing sensor nodes requires the calculation of several
               sensor schedules, a novel pruning algorithm that preserves
               optimal schedules is also derived in order to significantly reduce
               the computational demand. This is accomplished by exploiting
               the monotonicity of the Riccati equation and the information
               contribution of individual sensor nodes in combination with a
               branch-and-bound technique.}
}

@inproceedings{Fusion08_KlumppSawo-SGMF,
  author    = {Vesa Klumpp and Felix Sawo and Uwe D. Hanebeck and Dietrich Fr{\"a}nken},
  title     = {{The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_KlumppSawo-SGMF.pdf},
  pages     = {1--8},
  abstract  = {This paper addresses the efficient state estimation
               for mixed linear/nonlinear dynamic systems with noisy measurements.
               Based on a novel density representation - sliced Gaussian
               mixture density - the decomposition into a (conditionally) linear
               and nonlinear estimation problem is derived. The systematic
               approximation procedure minimizing a certain distance measure
               allows the derivation of (close to) optimal and deterministic
               estimation results. This leads to high-quality representations of
               the measurement-conditioned density of the states and, hence, to
               an overall more efficient estimation process. The performance of
               the proposed estimator is compared to state-of-the-art estimators,
               like the well-known marginalized particle filter.}
}

@inproceedings{Fusion08_Noack-ConvexSets,
  author    = {Benjamin Noack and Vesa Klumpp and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Nonlinear Bayesian Estimation with Convex Sets of Probability Densities}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_Noack.pdf},
  pages     = {1--8},
  abstract  = {This paper presents a theoretical framework for
               Bayesian estimation in the case of imprecisely known probability
               density functions. The lack of knowledge about the true density
               functions is represented by sets of densities. A formal Bayesian
               estimator for these sets is introduced, which is intractable for
               infinite sets. To obtain a tractable filter, properties of convex
               sets in form of convex polytopes of densities are investigated.
               It is shown that pathwise connected sets and their convex hulls
               describe the same ignorance. Thus, an exact algorithm is derived,
               which only needs to process the hull, delivering tractable results
               in the case of a proper parametrization. Since the estimator
               delivers a convex hull of densities as output, the theoretical
               grounds are laid for deriving efficient Bayesian estimators for
               sets of densities. The derived filter is illustrated by means of an
               example.}
}

@inproceedings{Fusion08_SawoKlumpp-SimParam,
  author    = {Felix Sawo and Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_SawoKlumpp-SimParam.pdf},
  pages     = {1--8},
  abstract  = {This paper presents a method for the simultaneous
               state and parameter estimation of finite-dimensional models of
               distributed systems monitored by a sensor network. In the
               first step, the distributed system is spatially and temporally
               decomposed leading to a linear finite-dimensional model in state
               space form. The main challenge is that the simultaneous state and
               parameter estimation of such systems leads to a high-dimensional
               nonlinear problem. Thanks to the linear substructure contained
               in the resulting finite-dimensional model, the development of an
               overall more efficient estimation process is possible. Therefore,
               in the second step, we propose the application of a novel density
               representation - sliced Gaussian mixture density - in order to
               decompose the estimation problem into a (conditionally) linear
               and a nonlinear problem. The systematic approximation procedure
               minimizing a certain distance measure allows the derivation
               of (close to) optimal and deterministic results. The proposed
               estimation process provides novel prospects in sensor network
               applications. The performance is demonstrated by means of
               simulation results.},
  timestamp = {2008.04.29}
}

@inproceedings{Fusion08_Wang,
  author    = {Hui Wang and Andrei Szabo and Joachim Bamberger and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pages     = {1--7},
  abstract  = {Indoor WLAN positioning should be modeled as a nonlinear
               and non-Gaussian dynamic system due to the complex indoor environment,
               radio propagation and motion behaviour. The aim of this paper is to
               analyze different filtering strategies for real life indoor WLAN
               positioning systems. The performance criteria for the comparison are
               the mean of localization errors and computational complexity.
               Three nonlinear filters are analyzed: Fourier density approximation (FF),
               particle filter (PF) and grid-based filter (GF), which are representatives for
               deterministic and random density approximation approaches.
               Our experimental results help to choose the appropriate filtering
               techniques under different resource limitations.},
  pdf       = {Fusion08_Wang.pdf},
  timestamp = {2008.05.05}
}

@inproceedings{ACC08_DeisenrothPeters-GPDPoriginal,
  author    = {Marc P. Deisenroth and Jan Peters and Carl E. Rasmussen},
  title     = {{A}pproximate {Dynamic Programming with Gaussian Processes}},
  booktitle = {Proceedings of the 2008 American Control Conference (ACC 2008)},
  year      = {2008},
  pages     = {4480--4485},
  address   = {Seattle, Washington, USA},
  month     = jun,
  abstract  = {In general, it is difficult to determine an optimal closed-loop policy
               in nonlinear control problems with continuous-valued state and control
               domains. Hence, approximations are often inevitable. The standard
               method of discretizing states and controls suffers from the curse
               of dimensionality and strongly depends on the chosen temporal sampling
               rate. In this paper, we introduce Gaussian process dynamic programming
               (GPDP) and determine an approximate globally optimal closed-loop
               policy. In GPDP, value functions in the Bellman recursion of the
               dynamic programming algorithm are modeled using Gaussian processes.
               GPDP returns an optimal state-feedback for a finite set of states.
               Based on these outcomes, we learn a possibly discontinuous closed-loop
               policy on the entire state space by switching between two independently
               trained Gaussian processes. A binary classifier selects one Gaussian
               process to predict the optimal control signal. We show that GPDP
               is able to yield an almost optimal solution to an LQ problem using
               few sample points. Moreover, we successfully apply GPDP to the underpowered
               pendulum swing up, a complex nonlinear control problem.},
  pdf       = {ACC08_DeisenrothPeters-GPDPoriginal.pdf}
}

@inproceedings{ESANN08_DeisenrothRasmussen-GPDPsysid,
  author    = {Marc P. Deisenroth and Carl E. Rasmussen and Jan Peters},
  title     = {{M}odel-{Based Reinforcement Learning with Continuous States and Actions}},
  booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008)},
  year      = {2008},
  pages     = {19--24},
  address   = {Bruges, Belgium},
  month     = apr,
  abstract  = {Finding an optimal policy in a reinforcement learning (RL) framework
               with continuous state and action spaces is challenging. Approximate
               solutions are often inevitable. GPDP is an approximate dynamic programming
               algorithm based on Gaussian process (GP) models for the value functions.
               In this paper, we extend GPDP to the case of unknown transition dynamics.
               After building a GP model for the transition dynamics, we apply GPDP
               to this model and determine a continuous-valued policy in the entire
               state space. We apply the resulting controller to the underpowered
               pendulum swing up. Moreover, we compare our results on this RL task
               to a nearly optimal discrete DP solution in a fully known environment.},
  pdf       = {ESANN08_DeisenrothRasmussen-GPDPsysid.pdf}
}

@incollection{Springer08_Lenz,
  booktitle = {{S}ignal {P}rocessing {T}echniques for {K}nowledge {E}xtraction and {I}nformation {F}usion},
  author    = {Henning Lenz and Bruno Betoni Parodi and Hui Wang and Andrei Szabo and Joachim Bamberger and Dragan Obradovic and Joachim Horn and Uwe D. Hanebeck},
  title     = {Adaptive Localization in Wireless Networks},
  editor    = {Danilo Mandic and Martin Golz and Anthony Luh and Dragan Obradovic and Toshihisa Tanaka},
  pages     = {97 -- 120},
  publisher = {Springer US},
  year      = {2008},
  abstract  = {Indoor positioning approaches based on communication systems typically use the received signal
               strength (RSS) as measurements. To work properly, such a system often requires many
               calibration points before its start. Based on theoretical-propagation models (RF planning)
               and on self-organizing maps (SOM) an adaptive approach for Simultaneous Localization and Learning (SLL)
               has been developed. The algorithm extracts out of online measurements the true model of the RF propagation.
               Applying SLL, a self-calibrating RSS-based positioning system with high accuracies can be realized
               without the need of cost intensive calibration measurements during system installation or
               maintenance. The main aspects of SLL are addressed as well as convergence and statistical
               properties. Results for real-world DECT and WLAN setups are given, showing that the localization
               starts with a basic performance slightly better than Cell-ID, finally reaching the accuracy of
               pattern matching using calibration points.},
  pdf       = {Fusion08_Lenz.pdf},
  doi       = {10.1007/978-0-387-74367-7_6},
  url       = {https://dx.doi.org/10.1007/978-0-387-74367-7_6}
}

@incollection{LNEE_ICINCO07_SawoRoberts,
  author    = {Felix Sawo and Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{Model-Based Reconstruction of Distributed Phenomena Using Discretized Representations of Partial Differential Equations}},
  booktitle = {Informatics in Control, Automation and Robotics, Selected Papers from ICINCO 2006, Series: Lecture Notes in Electrical Engineering},
  publisher = {Springer},
  year      = {2008},
  volume    = {15},
  abstract  = {This article addresses the model-based reconstruction and prediction of distributed phenomena
               characterized by partial differential equations, which are monitored by sensor networks. The novelty of the
               proposed reconstruction method is the systematic approach and the integrated treatment of uncertainties,
               which occur in the physical model and arise naturally from noisy measurements. By this means it is possible
               not only to reconstruct the entire phenomenon, even at non-measurement points, but also to reconstruct the
               complete density function of the state characterizing the distributed phenomenon. In the first step, the
               partial differential equation, i.e., distributed-parameter system, is spatially and temporally decomposed
               leading to a finite-dimensional state space form. In the next step, the state of the resulting lumped-parameter
               system, which provides an approximation of the solution of the underlying partial differential equation,
               is dynamically estimated under consideration of uncertainties. By using the estimation results, several
               additional tasks can be achieved by the sensor network, e.g. optimal sensor placement, optimal scheduling,
               model improvement, and system identification. The performance of the proposed model-based reconstruction
               method is demonstrated by means of simulations.},
  url       = {https://dx.doi.org/10.1007/978-3-540-79142-3_24}
}

@incollection{EWRL08RasmussenDeisenroth,
  title     = {Probabilistic {Inference for Fast Learning in Control}},
  pages     = {229--242},
  publisher = {Springer-Verlag},
  year      = {2008},
  editor    = {S. Girgin and M. Loth and R. Munos and P. Preux and D. Ryabko},
  author    = {Carl E. Rasmussen and Marc P. Deisenroth},
  volume    = {5323},
  series    = {Lecture Notes in Computer Science},
  month     = nov,
  abstract  = {We provide a novel framework for very fast model-based reinforcement
               learning in continuous state and action spaces. The framework requires
               probabilistic models that explicitly characterize their levels of confidence.
               Within this framework, we use flexible, non-parametric models to describe the
               world based on previously collected experience. We demonstrate learning on the
               cart-pole problem in a setting where we provide very limited prior knowledge
               about the task. Learning progresses rapidly, and a good policy is found after
               only a hand-full of iterations.},
  booktitle = {Recent {Advances in Reinforcement Learning}. Proceedings of the 8th European Workshop on Reinforcement Learning (EWRL 2008)},
  pdf       = {EWRL08_RasmussenDeisenroth.pdf},
  timestamp = {2008.11.04}
}

@inproceedings{CDC07_HanebeckSchrempf,
  author    = {Uwe D. Hanebeck and Oliver C. Schrempf},
  title     = {{G}reedy {A}lgorithms for {D}irac {M}ixture {A}pproximation of {A}rbitrary {P}robability {D}ensity {F}unctions},
  booktitle = {Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)},
  year      = {2007},
  pages     = {3065--3071},
  address   = {New Orleans, Louisiana, USA},
  month     = dec,
  abstract  = {Greedy procedures for suboptimal Dirac mixture approximation of an
               arbitrary probability density function are proposed, which approach
               the desired density by sequentially adding one component at a time.
               Similar to the batch solutions proposed earlier, a distance measure
               between the corresponding cumulative distributions is minimized by
               selecting the corresponding density parameters. This is due to the
               fact, that a distance between the densities is typically not well
               defined for Dirac mixtures. This paper focuses on the Cramer-von
               Mises distance, a weighted integral quadratic distance measure between
               the true distribution and its approximation. In contrast to the batch
               solutions, the computational complexity is much lower and grows only
               linearly with the number of components. Computational savings are
               even greater, when the required number of components, e.g., the minimum
               number of components for achieving a given quality measure, is not
               a priori known and must be searched for as well. The performance
               of the proposed sequential approximation approach is compared to
               that of the optimal batch solution.},
  pdf       = {CDC07_HanebeckSchrempf.pdf},
  timestamp = {2007.07.30}
}

@inproceedings{CDC07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{A} {N}onlinear {M}odel {P}redictive {C}ontrol {F}ramework {A}pproximating {N}oise {C}orrupted {S}ystems with {H}ybrid {T}ransition {D}ensities},
  booktitle = {Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)},
  year      = {2007},
  pages     = {3661--3666},
  address   = {New Orleans, Louisiana, USA},
  month     = dec,
  abstract  = {In this paper, a framework for Nonlinear Model Predictive Control
               (NMPC) for heavily noise-affected systems is presented. Within this
               framework, the noise influence, which originates from uncertainties
               during model identification or measurement, is explicitly considered.
               This leads to a significant increase in the control quality. One
               part of the proposed framework is the efficient state prediction,
               which is necessary for NMPC. It is based on transition density approximation
               by hybrid transition densities, which allows efficient closed-form
               state prediction of time-variant nonlinear systems with continuous
               state spaces in discrete time. Another part of the framework is a
               versatile value function representation using Gaussian mixtures,
               Dirac mixtures, and even a combination of both. Based on these methods,
               an efficient closed-form algorithm for calculating an approximate
               value function of the NMPC optimal control problem employing dynamic
               programming is presented. Thus, also very long optimization horizons
               can be used and furthermore it is possible to calculate the value
               function off-line, which reduces the on-line computational burden
               significantly. The capabilities of the framework and especially the
               benefits that can be gained by incorporating the noise in the controller
               are illustrated by the example of a miniature walking robot following
               a given path.},
  pdf       = {CDC07_WeisselHuber.pdf},
  timestamp = {2007.07.30}
}

@inproceedings{IROS07_SchrempfAlbrecht,
  author    = {Oliver C. Schrempf and David Albrecht and Uwe D. Hanebeck},
  title     = {{T}ractable {P}robabilistic {M}odels for {I}ntention {R}ecognition {B}ased on {E}xpert {K}nowledge},
  booktitle = {Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)},
  year      = {2007},
  pages     = {1429--1434},
  address   = {San Diego, California, USA},
  month     = nov,
  abstract  = {Intention recognition is an important topic in human-robot cooperation
               that can be tackled using probabilistic model-based methods. A popular
               instance of such methods are Bayesian networks where the dependencies
               between random variables are modeled by means of a directed graph.
               Bayesian networks are very efficient for treating networks with conditionally
               independent parts. Unfortunately, such independence sometimes has
               to be constructed by introducing so called hidden variables with
               an intractably large state space. An example are human actions which
               depend on human intentions and on other human actions. Our goal in
               this paper is to find models for intention-action mapping with a
               reduced state space in order to allow for tractable on-line evaluation.
               We present a systematic derivation of the reduced model and experimental
               results of recognizing the intention of a real human in a virtual
               environment.},
  pdf       = {IROS07_SchrempfAlbrecht.pdf}
}

@inproceedings{IROS07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{T}est-{E}nvironment based on a {T}eam of {M}iniature {W}alking {R}obots for {E}valuation of {C}ollaborative {C}ontrol {M}ethods},
  booktitle = {Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)},
  year      = {2007},
  pages     = {2474--2479},
  address   = {San Diego, California, USA},
  month     = nov,
  abstract  = {For the collaborative control of a team of robots, a set of well-suited
               high-level control algorithms, especially for path planning and measurement
               scheduling, is essential. The quality of these control algorithms
               can be significantly increased by considering uncertainties that
               arise, e.g. from noisy measurements or system model abstraction,
               by incorporating stochastic filters into the control. To develop
               these kinds of algorithms and to prove their effectiveness, obviously
               real-world experiments with real world uncertainties are mandatory.
               Therefore, a test-environment for evaluating algorithms for collaborative
               control of a team of robots is presented. This test-environment is
               founded on miniature walking robots with six degrees of freedom.
               Their novel locomotion concept not only allows them to move in a
               wide variety of different motion patterns far beyond the possibilities
               of traditionally employed wheel-based robots, but also to handle
               real-world conditions like uneven ground or small obstacles. These
               robots are embedded in a modular test-environment, comprising infrastructure
               and simulation modules as well as a high-level control module with
               submodules for pose estimation, path planning, and measurement scheduling.
               The interaction of the individual modules of the introduced test-environment
               is illustrated by an experiment from the field of cooperative localization
               with focus on measurement scheduling, where the robots that perform
               distance measurements are selected based on a novel criterion, the
               normalized mutual Mahalanobis distance.},
  pdf       = {IROS07_WeisselHuber.pdf}
}

@inproceedings{SDF07_Huber,
  author    = {Marco F. Huber and Eric Stiegeler and Uwe D. Hanebeck},
  title     = {{O}n {S}ensor {S}cheduling in {C}ase of {U}nreliable {C}ommunication},
  booktitle = {INFORMATIK 2007 - the 37th Annual Conference of the Gesellschaft f{\"u}r Informatik e.V. (GI), 3rd German Workshop Sensor Data Fusion: Trends, Solutions, Applications (SDF 2007)},
  year      = {2007},
  pages     = {90--94},
  address   = {Bremen, Germany},
  month     = sep,
  abstract  = {This paper deals with the linear discrete-time sensor scheduling problem
               in unreliable communication networks. In case of the common assumption
               of an error-free communication, the sensor scheduling problem, where
               one sensor from a sensor network is selected for measuring at a specific
               time instant so that the estimation errors are minimized, can be
               solved off-line by extensive tree search. For the more realistic
               scenario, where communication is unreliable, a scheduling approach
               using a prioritization list for the sensors is proposed that leads
               to a minimization of the estimation error by selecting the most beneficial
               sensor on-line. To lower the computational demand for the priority
               list calculation, a novel optimal pruning approach is introduced.},
  keywords  = {Sensor Scheduling},
  pdf       = {SDF07_Huber.pdf},
  timestamp = {2007.06.18}
}

@inproceedings{ACC07_Huber,
  author    = {Marco F. Huber and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{E}fficient {N}onlinear {M}easurement {U}pdating based on {G}aussian {M}ixture {A}pproximation of {C}onditional {D}ensities},
  booktitle = {Proceedings of the 2007 American Control Conference (ACC 2007)},
  year      = {2007},
  pages     = {4425--4430},
  address   = {New York, New York, USA},
  month     = jul,
  abstract  = {Filtering or measurement updating for nonlinear stochastic dynamic
               systems requires approximate calculations, since an exact solution
               is impossible to obtain in general. We propose a Gaussian mixture
               approximation of the conditional density, which allows performing
               measurement updating in closed form. The conditional density is a
               probabilistic representation of the nonlinear system and depends
               on the random variable of the measurement given the system state.
               Unlike the likelihood, the conditional density is independent of
               actual measurements, which permits determining its approximation
               off-line. By treating the approximation task as an optimization problem,
               we use progressive processing to achieve high quality results. Once
               having calculated the conditional density, the likelihood can be
               determined on-line, which, in turn, offers an efficient approximate
               filter step. As result, a Gaussian mixture representation of the
               posterior density is obtained. The exponential growth of Gaussian
               mixture components resulting from repeated filtering is avoided implicitly
               by the prediction step using the proposed techniques.},
  keywords  = {System Approximation, Gaussian Mixtures},
  pdf       = {ACC07_Huber-GMLikelihood.pdf}
}

@inproceedings{ACC07_Schrempf-DiracMixt,
  author       = {Oliver C. Schrempf and Uwe D. Hanebeck},
  booktitle    = {Proceedings of the 2007 American Control Conference (ACC 2007)},
  title        = {{R}ecursive {P}rediction of {S}tochastic {N}onlinear {S}ystems {B}ased on {O}ptimal {D}irac {M}ixture {A}pproximations},
  year         = {2007},
  address      = {New York, New York, USA},
  month        = jul,
  pages        = {1768--1774},
  abstract     = {This paper introduces a new approach to the recursive propagation
                  of probability density functions through discrete-time stochastic
                  nonlinear dynamic systems. An efficient recursive procedure is proposed
                  that is based on the optimal approximation of the posterior densities
                  after each prediction step by means of Dirac mixtures. The parameters
                  of the individual components are selected by systematically minimizing
                  a suitable distance measure in such a way that the future evolution
                  of the approximate densities is as close to the exact densities as
                  possible.},
  howpublished = {submitted},
  keywords     = {Dirac Mixtures},
  pdf          = {ACC07_Schrempf-DiracMixt.pdf}
}

@inproceedings{ACC07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{E}fficient {C}ontrol of {N}onlinear {N}oise--{C}orrupted {S}ystems {U}sing a {N}ovel {M}odel {P}redictive {C}ontrol {F}ramework},
  booktitle = {Proceedings of the 2007 American Control Conference (ACC 2007)},
  year      = {2007},
  pages     = {3751--3756},
  address   = {New York, New York, USA},
  month     = jul,
  abstract  = {Model identification and measurement acquisition is always to some
               degree uncertain. Therefore, a framework for Nonlinear Model Predictive
               Control (NMPC) is proposed that explicitly considers the noise influence
               on nonlinear dynamic systems with continuous state spaces and a finite
               set of control inputs in order to significantly increase the control
               quality. Integral parts of NMPC are the prediction of system states
               over a finite horizon as well as the problem specific modeling of
               reward functions. For achieving an efficient and also accurate state
               prediction, the introduced framework uses transition densities approximated
               by means of axis-aligned Gaussian mixtures. The representation power
               of Gaussian mixtures is also used to model versatile reward functions.
               Thus, together with the prediction technique a closed-form calculation
               of the optimization problems arising from NMPC is possible. Additionally,
               an efficient algorithm for calculating an approximate value function
               of the corresponding optimal control problem employing dynamic programming
               is presented. Thus, the value function can be calculated off-line,
               which reduces the on-line computational burden significantly and
               also permits the use of long optimization horizons. The capabilities
               of the framework and especially the benefits that can be gained by
               incorporating the noise in the controller are illustrated by the
               example of a two-wheeled differential-drive mobile robot following
               a given path.},
  pdf       = {ACC07_WeisselHuber.pdf}
}

@inproceedings{ECC07_DeisenrothWeissel,
  author    = {Marc P. Deisenroth and Florian Weissel and Toshiyuki Ohtsuka and Uwe D. Hanebeck},
  title     = {{Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces}},
  booktitle = {Proceedings of the 2007 European Control Conference (ECC 2007)},
  year      = {2007},
  address   = {Kos, Greece},
  month     = jul,
  abstract  = {A novel online-computation approach to optimal control of nonlinear,
               noise-affected systems with continuous state and control spaces is
               presented. In the proposed algorithm, system noise is explicitly
               incorporated into the control decision. This leads to superior results
               compared to state-of-the-art nonlinear controllers that neglect this
               influence. The solution of an optimal nonlinear controller for a
               corresponding deterministic system is employed to find a meaningful
               state space restriction. This restriction is obtained by means of
               approximate state prediction using the noisy system equation. Within
               this constrained state space, an optimal closed-loop solution for
               a finite decisionmaking horizon (prediction horizon) is determined
               within an adaptively restricted optimization space. Interleaving
               stochastic dynamic programming and value function approximation yields
               a solution to the considered optimal control problem. The enhanced
               performance of the proposed discrete-time controller is illustrated
               by means of a scalar example system. Nonlinear model predictive control
               is applied to address approximate treatment of infinite-horizon problems
               by the finite-horizon controller.},
  pdf       = {ECC07_DeisenrothWeissel.pdf}
}

@inproceedings{Fusion07_HanselmannSchrempf,
  author    = {Anne Hanselmann and Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{O}ptimal {P}arametric {D}ensity {E}stimation by {M}inimizing an {A}nalytic {D}istance {M}easure},
  booktitle = {Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)},
  year      = {2007},
  address   = {Québec, Canada},
  month     = jul,
  abstract  = {In this paper, we present a novel approach to parametric density estimation
               from given samples. The samples are treated as a parametric density
               function by means of a Dirac mixture, which allows for applying analytic
               optimization techniques. The method is based on minimizing a distance
               measure between the integral of the approximation function and the
               empirical cumulative distribution function (EDF) of the given samples,
               where the EDF is represented by the integral of the Dirac mixture.
               Since this minimization problem cannot be solved directly in general,
               a progression technique is applied. Increased performance of the
               approach in comparison to iterative maximum likelihood approaches
               is shown in simulations.},
  pdf       = {Fusion07_HanselmannSchrempf.pdf}
}

@inproceedings{Fusion07_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{T}he {H}ybrid {D}ensity {F}ilter for {N}onlinear {E}stimation based on {H}ybrid {C}onditional {D}ensity {A}pproximation},
  booktitle = {Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)},
  year      = {2007},
  address   = {Québec, Canada},
  month     = jul,
  abstract  = {In nonlinear Bayesian estimation it is generally inevitable to incorporate
               approximate descriptions of the exact estimation algorithm. There
               are two possible ways to involve approximations: Approximating the
               nonlinear stochastic system model or approximating the prior probability
               density function. The key idea of the introduced novel estimator
               called Hybrid Density Filter relies on approximating the nonlinear
               system, thus approximating conditional densities. These densities
               nonlinearly relate the current system state to the future system
               state at predictions or to potential measurements at measurement
               updates. A hybrid density consisting of both Dirac delta functions
               and Gaussian densities is used for an optimal approximation. This
               paper addresses the optimization problem for treating the conditional
               density approximation. Furthermore, efficient estimation algorithms
               are derived based upon the special structure of the hybrid density,
               which yield a Gaussian mixture representation of the system state's
               density.},
  keywords  = {System Approximation, Hybrid Density},
  pdf       = {Fusion07_Huber.pdf}
}

@inproceedings{Fusion07_SawoHuber-ParamIdent,
  author    = {Felix Sawo and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{P}arameter {I}dentification and {R}econstruction for {D}istributed {P}henomena {B}ased on {H}ybrid {D}ensity {F}ilter},
  booktitle = {Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)},
  year      = {2007},
  address   = {Québec, Canada},
  month     = jul,
  abstract  = {This paper addresses the problem of model-based reconstruction and
               parameter identification of distributed phenomena characterized by
               partial differential equations. The novelty of the proposed method
               is the systematic approach and the integrated treatment of uncertainties,
               which naturally occur in the physical system and arise from noisy
               measurements. The main challenge of accurate reconstruction is that
               model parameters, i.e., diffusion coefficients, of the physical model
               are not known in advance and usually need to be identified. Generally,
               the problem of parameter identification leads to a nonlinear estimation
               problem. Hence, a novel efficient recursive procedure is employed.
               Unlike other estimators, the so-called Hybrid Density Filter not
               only assures accurate estimation results for nonlinear systems, but
               also offers an efficient processing. By this means it is possible
               to reconstruct and identify distributed phenomena monitored by autonomous
               wireless sensor networks. The performance of the proposed estimation
               method is demonstrated by means of simulations.},
  pdf       = {Fusion07_SawoHuber-ParamIdent.pdf}
}

@inproceedings{HCI07_Wang,
  author    = {Hui Wang and Henning Lenz and Andrei Szabo and Joachim Bamberger and Uwe D. Hanebeck},
  title     = {{E}nhancing the {M}ap {U}sage for {I}ndoor {L}ocation-{A}ware {S}ystems},
  booktitle = {International Conference on Human-Computer Interaction (HCI 2007)},
  year      = {2007},
  address   = {Peking, China},
  month     = jul,
  abstract  = {Location-aware systems are receiving more and more interest in both
               academia and industry due to their promising prospective in a broad
               category of so-called Location-Based-Services (LBS). The map interface
               plays a crucial role in the location-aware systems, especially for
               indoor scenarios. This paper addresses the usage of map information
               in a Wireless LAN (WLAN)-based indoor navigation system. We describe
               the benefit of using maNMp information in multiple algorithms of
               the system, including radio-map generation, tracking, semantic positioning
               and navigation. Then we discuss how to represent or model the indoor
               map to fulfill the requirements of intelligent algorithms. We believe
               that a vector-based multi-layer representation is the best choice
               for indoor location-aware system.},
  pdf       = {HCI07_Wang.pdf}
}

@inproceedings{CARS07_Roberts,
  author    = {Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{M}otion {E}stimation and {R}econstruction of a {H}eart {S}urface by {M}eans of 2{D}-/3{D}- {M}embrane {M}odels},
  booktitle = {Proceedings of 21st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2007)},
  year      = {2007},
  pages     = {243--245},
  address   = {Berlin, Germany},
  month     = jun,
  abstract  = {In order to assist surgeons during minimally invasive interventions
               on the beating heart, it would be helpful to develop a robotic surgery
               system, which synchronizes the instruments with the heart surface,
               so that their positions do not change relative to the point of interest
               (POI). The synchronization of the robotic manipulators requires an
               estimation of the heart surface motion. In this paper, a modelbased
               motion estimation of the heart surface is presented. The motion of
               a partition of the heart surface is modelled by means of a thin or
               thick vibrating membrane in order to represent the epicardial surface
               or the connected epicard and myocard. The membrane motion is described
               by means of a system of coupled linear partial differential equations
               (PDEs), whose 3D-input function is assumed to be known. After spatial
               discretization of the PDE solution space by the Finite Spectral Element
               Method, a bank of lumped systems is obtained. A Kalman filter is
               used to estimate the state of the lumped systems by incorporating
               noisy measurements of the heart surface. Measurements can be the
               position or velocity of sonomicrometry-based sensors or of certain
               landmarks, which are tracked by optical sensors. With the model-based
               estimation it is possible to reconstruct the entire partition of
               the heart surface even at non-measurement points and thus at each
               POI.},
  pdf       = {CARS07_Roberts.pdf}
}

@inproceedings{ICINCO07_Schrempf-DiracMixt,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{A} {S}tate {E}stimator for {N}onlinear {S}tochastic {S}ystems {B}ased on {D}irac {M}ixture {A}pproximations},
  booktitle = {Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)},
  year      = {2007},
  volume    = {SPSMC},
  pages     = {54--61},
  address   = {Angers, France},
  month     = may,
  abstract  = {This paper presents a filter approach for estimating the state of
               nonlinear dynamic systems based on recursive approximation of posterior
               densities by means of Dirac mixture functions. The filter consists
               of a prediction step and a filter step. The approximation approach
               is based on a systematic minimization of a distance measure and is
               hence optimal and deterministic. In contrast to non-deterministic
               methods we are able to determine the optimal number of components
               in the Dirac mixture. A further benefit of the proposed approach
               is the consideration of measurements during the approximation process
               in order to avoid parameter degradation.},
  keywords  = {Dirac Mixtures},
  pdf       = {ICINCO07_Schrempf-DiracMixt.pdf},
  timestamp = {2007.05.30}
}

@inproceedings{ICINCO07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{A} {C}losed--{F}orm {M}odel {P}redictive {C}ontrol {F}ramework for {N}onlinear {N}oise--{C}orrupted {S}ystems},
  booktitle = {Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)},
  year      = {2007},
  volume    = {SPSMC},
  pages     = {62--69},
  address   = {Angers, France},
  month     = may,
  abstract  = {In this paper, a framework for Nonlinear Model Predictive Control
               (NMPC) that explicitly incorporates the noise influence on systems
               with continuous state spaces is introduced. By the incorporation
               of noise, which results from uncertainties during model identification
               and the measurement process, the quality of control can be significantly
               increased. Since NMPC requires the prediction of system states over
               a certain horizon, an efficient state prediction technique for nonlinear
               noise-affected systems is required. This is achieved by using transition
               densities approximated by axis-aligned Gaussian mixtures together
               with methods to reduce the computational burden. A versatile cost
               function representation also employing Gaussian mixtures provides
               an increased freedom of modeling. Combining the prediction technique
               with this value function representation allows closed-form calculation
               of the necessary optimization problems arising from NMPC. The capabilities
               of the framework and especially the benefits that can be gained by
               considering the noise in the controller are illustrated by the example
               of a mobile robot following a given path.},
  pdf       = {ICINCO07_WeisselHuber.pdf}
}

@inproceedings{ICRA07_Bader,
  author    = {Thomas Bader and Alexander Wiedemann and Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{M}odel--based {M}otion {E}stimation of {E}lastic {S}urfaces for {M}inimally {I}nvasive {C}ardiac {S}urgery},
  booktitle = {Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA 2007)},
  year      = {2007},
  pages     = {2261--2266},
  address   = {Rome, Italy},
  month     = apr,
  abstract  = {In order to assist surgeons during surgery on moving organs, e.g.
               minimally invasive beating heart bypass surgery, a master-slave system
               which synchronizes surgical instruments with the organ's motion is
               desired. This synchronization requires reliable estimation of the
               organ's motion. In this paper, we present a new approach to motion
               estimation based on a state motion model for a partition of the heart's
               surface. Its motion behavior is described by a partial differential
               equation whose input function is assumed to be periodic. An estimator
               is used on one hand to predict future model states based on reconstruction
               of the input function and on the other hand to incorporate noisy
               spatially discrete measurements in order to improve state estimation.
               The model-based motion estimation is evaluated using a simple heart
               simulator. Measurements are obtained by reconstructing 3D position
               of markers on a pulsating membrane by means of a stereo camera system.},
  doi       = {10.1109/ROBOT.2007.363656},
  pdf       = {ICRA07_Bader.pdf}
}

@inproceedings{IPSN07_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{H}ybrid {T}ransition {D}ensity {A}pproximation for {E}fficient {R}ecursive {P}rediction of {N}onlinear {D}ynamic {S}ystems},
  booktitle = {International Conference on Information Processing in Sensor Networks (IPSN 2007)},
  year      = {2007},
  pages     = {283--292},
  address   = {Cambridge, Massachusetts, USA},
  month     = apr,
  abstract  = {For several tasks in sensor networks, such as localization, information
               fusion, or sensor scheduling, Bayesian estimation is of paramount
               importance. Due to the limited computational and memory resources
               of the nodes in a sensor network, evaluation of the prediction step
               of the Bayesian estimator has to be performed very efficiently. An
               exact and closed-form representation of the predicted probability
               density function of the system state is typically impossible to obtain,
               since exactly solving the prediction step for nonlinear discrete-time
               dynamic systems in closed form is unfeasible. Assuming additive noise,
               we propose an accurate approximation of the predicted density, that
               can be calculated efficiently by optimally approximating the transition
               density using a hybrid density. A hybrid density consists of two
               different density types: Dirac delta functions that cover the domain
               of the current density of the system state, and another density type,
               e.g. Gaussian densities, that cover the domain of the predicted density.
               The freely selectable, second density type of the hybrid density
               depends strongly on the noise affecting the nonlinear system. So,
               the proposed approximation framework for nonlinear prediction is
               not restricted to a specific noise density. It further allows an
               analytical evaluation of the Chapman-Kolmogorov prediction equation
               and can be interpreted as a deterministic sampling estimation approach.
               In contrast to methods using random sampling like particle filters,
               a dramatic reduction in the number of components and a subsequent
               decrease in computation time for approximating the predicted density
               is gained.},
  keywords  = {System Approximation, Hybrid Density},
  pdf       = {IPSN2007_Huber.pdf}
}

@article{ITM07_BrunnSawo,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{M}odellbasierte {V}ermessung verteilter {P}h{\"a}nomene und {G}enerierung optimaler {M}esssequenzen},
  journal   = {tm -- Technisches Messen, Oldenbourg Verlag},
  year      = {2007},
  volume    = {3},
  pages     = {75--90},
  month     = mar,
  abstract  = {Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
               verteilter physikalischer Ph{\"a}nomene. Diese Methoden zeichnen
               sich durch eine systematische Behandlung von Unsicherheiten aus,
               so dass neben der Rekonstruktion der vollst{\"a}ndigen Wahrscheinlichkeitsdichte
               der relevanten Gr{\"o}\ss{}en aus einer geringen Anzahl von zeit-,
               orts- und wertdiskreten Messungen auch die Generierung optimaler
               Messsequenzen m{\"o}glich ist. Es wird dargestellt, wie eine Beschreibung
               f{\"u}r ein verteilt"=parametrisches System in Form einer partiellen
               Differentialgleichung, welche einen unendlich-dimensionalen Zustandsraum
               beschreibt, in eine konzentriert-parametrische Form konvertiert wird.
               Diese kann als Grundlage f{\"u}r den Entwurf klassischer Sch{\"a}tzer,
               wie z. B. des Kalman-Filters, dienen. Ferner wird eine Methode zur
               Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
               Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
               die Unsicherheit auf ein gew{\"u}nschtes Ma\ss{} zu reduzieren. Die
               Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
               und der Verformung einer F{\"u}hrungsschiene, demonstriert. Zus{\"a}tzlich
               werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
               und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
               beim Einsatz von Sensornetzwerken auftreten, diskutiert.},
  booktitle = {Informationsfusion in der Mess- und Sensortechnik},
  editor    = {J{\"u}rgen Beyerer and Fernando {Puente Le{\'o}n} and Klaus-Dieter Sommer},
  pdf       = {ITM07_BrunnSawo.pdf},
  publisher = {Universit{\"a}tsverlag Karlsruhe}
}

@inproceedings{WPNC07_Wang,
  author    = {Hui Wang and Henning Lenz and Andrei Szabo and Joachim Bamberger and Uwe D. Hanebeck},
  title     = {{WLAN}--{B}ased {P}edestrian {T}racking {U}sing {P}article {F}ilters and {L}ow--{C}ost {MEMS} {S}ensors},
  booktitle = {Workshop on Positioning, Navigation and Communication, (WPNC 2007)},
  year      = {2007},
  address   = {Hannover, Germany},
  month     = mar,
  pdf       = {WPNC07_Wang.pdf},
  abstract  = {Indoor positioning systems based on Wireless LAN
               (WLAN) are being widely investigated in academia and industry.
               Meanwhile, the emerging low-cost MEMS sensors can also be used
               as another independent positioning source. In this paper, we
               propose a pedestrian tracking framework based on particle filters,
               which extends the typical WLAN-based indoor positioning systems
               by integrating low-cost MEMS accelerometer and map
               information. Our simulation and real world experiments indicate a
               remarkable performance improvement by using this fusion
               framework.}
}

@inproceedings{CDC06_Brunn-Fourier,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{N}onlinear {M}ultidimensional {B}ayesian {E}stimation with {F}ourier {D}ensities},
  booktitle = {Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)},
  year      = {2006},
  pages     = {1303--1308},
  address   = {San Diego, California, USA},
  month     = dec,
  abstract  = {Efficiently implementing nonlinear Bayesian estimators is still an
               unsolved problem, especially for the multidimensional case. A trade-off
               between estimation quality and demand on computational resources
               has to be found. Using multidimensional Fourier series as representation
               for probability density functions, so called Fourier densities, is
               proposed. To ensure non-negativity, the approximation is performed
               indirectly via Psi-densities, of which the absolute square represent
               the Fourier density. It is shown that PSI-densities can be determined
               using the efficient fast Fourier transform algorithm and their coefficients
               have an ordering with respect to the Hellinger metric. Furthermore,
               the multidimensional Bayesian estimator based on Fourier Densities
               is derived in closed form. That allows an efficient realization of
               the Bayesian estimator where the demands on computational resources
               are adjustable.},
  pdf       = {CDC06_Brunn-Fourier.pdf}
}

@inproceedings{CDC06_Schrempf-DiracMixt,
  author    = {Oliver C. Schrempf and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{D}ensity {A}pproximation {B}ased on {D}irac {M}ixtures with {R}egard to {N}onlinear {E}stimation and {F}iltering},
  booktitle = {Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)},
  year      = {2006},
  address   = {San Diego, California, USA},
  month     = dec,
  abstract  = {A deterministic procedure for optimal approximation of arbitrary probability
               density functions by means of Dirac mixtures with equal weights is
               proposed. The optimality of this approximation is guaranteed by minimizing
               the distance of the approximation from the true density. For this
               purpose a distance measure is required, which is in general not well
               defined for Dirac mixtures. Hence, a key contribution is to compare
               the corresponding cumulative distribution functions. This paper concentrates
               on the simple and intuitive integral quadratic distance measure.
               For the special case of a Dirac mixture with equally weighted components,
               closed-form solutions for special types of densities like uniform
               and Gaussian densities are obtained. Closed-form solution of the
               given optimization problem is not possible in general. Hence, another
               key contribution is an efficient solution procedure for arbitrary
               true densities based on a homotopy continuation approach. In contrast
               to standard Monte Carlo techniques like particle filters that are
               based on random sampling, the proposed approach is deterministic
               and ensures an optimal approximation with respect to a given distance
               measure. In addition, the number of required components (particles)
               can easily be deduced by application of the proposed distance measure.
               The resulting approximations can be used as basis for recursive nonlinear
               filtering mechanism alternative to Monte Carlo methods.},
  keywords  = {Dirac Mixtures},
  pdf       = {CDC06_Schrempf-DiracMixt.pdf}
}

@inproceedings{CCA06_Sawo-ParameterizedGM,
  author    = {Felix Sawo and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{P}arameterized {J}oint {D}ensities with {G}aussian {M}ixture {M}arginals and their {P}otential {U}se in {N}onlinear {R}obust {E}stimation},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006)},
  year      = {2006},
  address   = {Munich, Germany},
  month     = oct,
  abstract  = {This paper addresses the challenges of the fusion of two random vectors
               with imprecisely known stochastic dependency. This problem mainly
               occurs in decentralized estimation, e.g. of a distributed phenomenon,
               where the stochastic dependencies between the individual states are
               not stored. To cope with such problems we propose to exploit parameterized
               joint densities with both Gaussian marginals and Gaussian mixture
               marginals. Under structural assumptions these parameterized joint
               densities contain all information about the stochastic dependencies
               between their marginal densities in terms of a generalized correlation
               parameter vector xi. The parameterized joint densities are applied
               to the prediction step and the measurement step under imprecisely
               known correlation leading to a whole family of possible estimation
               results. The resulting density functions are characterized by the
               generalized correlation parameter vector xi. Once this structure
               and the bounds of these parameters are known, it is possible to find
               bounding densities containing all possible density functions, i.e.,
               conservative estimation results.},
  pdf       = {CCA06_Sawo-ParameterizedGM.pdf}
}

@inproceedings{HCRS06_RoesslerSchrempf,
  author    = {Patrick R{\"o}\ss{}ler and Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{S}tochastic {P}rediction of {W}aypoints for {E}xtended-{R}ange {T}elepresence {A}pplications},
  booktitle = {2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)},
  year      = {2006},
  pages     = {85--89},
  address   = {Munich, Germany},
  month     = oct,
  pdf       = {HCRS06_RoesslerSchrempf.pdf},
  abstract  = {The Motion Compression framework for extended range telepresence
               applications consists of three functional modules:
               path prediction, path transformation, and user guidance. This
               paper presents a new path prediction module for known environments
               that exploits the property, that humans typically
               walk on straight paths toward discrete goal objects. In order
               to estimate the user's goal object out of a set of possible goals,
               we derived a Bayesian filter that gives this discrete estimate
               based on continuous measurements of the user's head pose.}
}

@inproceedings{HCRS06_SchrempfSchmid,
  author    = {Andreas J. Schmid and Oliver C. Schrempf and Heinz W{\"o}rn and Uwe D. Hanebeck},
  title     = {{T}owards {I}ntuitive {H}uman-{R}obot {C}ooperation},
  booktitle = {2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)},
  year      = {2006},
  pages     = {7--12},
  address   = {Munich, Germany},
  month     = oct,
  pdf       = {HCRS06_SchrempfSchmid.pdf},
  abstract  = {Human-robot cooperation calls for the treatment of human-machine
               communication channels, especially if humanoid robots
               are involved. In this paper, we consider implicit nonverbal
               channels given by recognizing the partner's intention
               and proactive execution of tasks. We propose a method that
               keeps the human in the loop and allows for the systematic reduction
               of uncertainty inherent in implicit cooperation. We
               present a benchmark scenario as well as preliminary implementation
               results.}
}

@inproceedings{IROS06_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Uwe D. Hanebeck},
  title     = {{S}imultaneous {M}otion {C}ompression for {M}ulti--{U}ser {E}xtended {R}ange {T}elepresence},
  booktitle = {Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006)},
  year      = {2006},
  pages     = {5189--5194},
  address   = {Beijing, China},
  month     = oct,
  doi       = {10.1109/IROS.2006.281656},
  pdf       = {IROS06_Roessler.pdf},
  abstract  = {Extended range telepresence allows a human user
               to intuitively teleoperate a mobile robot through arbitrarily large
               remote environments by natural walking. In order to give the user
               the possibility to navigate the robot through an arbitrarily large
               remote environments, while his own environment is of limited
               size, Motion Compression is used. The Motion Compression
               framework provides a nonlinear transformation between the
               user's path and the robot's path, which preserves path length and
               turning angles. There is, however, a difference in path curvature,
               which is minimized in order to guarantee a high degree of
               immersion.
               A major drawback of the current system is its inability to
               deal with non-convex time-variant environments or environments
               shared by multiple users. This paper presents a systematic
               approach to extending Motion Compression to non-convex environments.
               This solution will then be used to cover the multi-user
               case.}
}

@incollection{IMS06_BrunnSawo,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{I}nformationsfusion f{\"u}r verteilte {S}ysteme},
  booktitle = {Informationsfusion in der Mess- und Sensortechnik},
  publisher = {Universit{\"a}tsverlag Karlsruhe},
  year      = {2006},
  editor    = {J{\"u}rgen Beyerer and Fernando {Puente Le{\'o}n} and Klaus-Dieter Sommer},
  pages     = {75--90},
  month     = sep,
  abstract  = {Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
               verteilter physikalischer Ph{\"a}nomene. Diese Methoden zeichnen
               sich durch eine systematische Behandlung stochastischer Unsicherheiten
               aus, so dass neben der Rekonstruktion der vollst{\"a}ndigen Wahrscheinlichkeitsdichte
               der relevanten Gr{\"o}\ss{}en aus einer geringen Anzahl von zeit-,
               orts- und wertdiskreten Messungen auch die Generierung optimaler
               Messsequenzen m{\"o}glich ist. Es wird dargestellt, wie eine Beschreibung
               f{\"u}r ein verteilt"=parametrisches System in Form einer partiellen
               Differentialgleichung, welche einen unendlich"=dimensionalen Zustandsraum
               beschreibt, in eine konzentriert"=parametrische Form konvertiert
               wird. Diese kann als Grundlage f{\"u}r den Entwurf klassischer Sch{\"a}tzer,
               wie z.B. des Kalman Filters, dienen. Ferner wird eine Methode zur
               Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
               Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
               die Unsicherheit auf ein gew{\"u}nschtes Ma\ss{} zu reduzieren. Die
               Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
               und der Verformung einer F{\"u}hrungsschiene, demonstriert. Zus{\"a}tzlich
               werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
               und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
               beim Einsatz von Sensornetzwerken auftreten, diskutiert.},
  pdf       = {IMS06_BrunnSawo.pdf}
}

@inproceedings{MFI06_Beutler,
  author    = {Frederik Beutler and Uwe D. Hanebeck},
  title     = {{T}he {P}robabilistic {I}nstantaneous {M}atching {A}lgorithm},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {311--316},
  address   = {Heidelberg, Germany},
  month     = sep,
  abstract  = {A new Bayesian filtering technique for estimating signal parameters
               directly from discrete-time sequences is introduced. The so called
               probabilistic instantaneous matching algorithm recursively updates
               the probability density function of the parameters for every received
               sample and, thus, provides a high update rate up to the sampling
               rate with high accuracy. In order to do so, one of the signal sequences
               is used as part of a time-variant nonlinear measurement equation.
               Furthermore, the time-variant nature of the parameters is explicitly
               considered via a system equation, which describes the evolution of
               the parameters over time. An important feature of the probabilistic
               instantaneous matching algorithm is that it provides a probability
               density function over the parameter space instead of a single point
               estimate. This probability density function can be used in further
               processing steps, e.g. a range based localization algorithm in the
               case of time-of-arrival estimation.},
  pdf       = {MFI06_Beutler.pdf}
}

@inproceedings{MFI06_Brunn-Fourier,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{E}fficient {N}onlinear {B}ayesian {E}stimation based on {F}ourier {D}ensities},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {312--322},
  address   = {Heidelberg, Germany},
  month     = sep,
  abstract  = {Efficiently implementing nonlinear Bayesian estimators is still not
               a fully solved problem. For practical applications, a trade-off between
               estimation quality and demand on computational resources has to be
               found. In this paper, the use of nonnegative Fourier series, so-called
               Fourier densities, for Bayesian estimation is proposed. By using
               the absolute square of Fourier series for the density representation,
               it is ensured that the density stays nonnegative. Nonetheless, approximation
               of arbitrary probability density functions can be made by using the
               Fourier integral formula. An efficient Bayesian estimator algorithm
               with constant complexity for nonnegative Fourier series is derived
               and demonstrated by means of an example.},
  pdf       = {MFI06_Brunn-Fourier.pdf}
}

@inproceedings{MFI06_DeisenrothWeissel,
  author    = {Marc P. Deisenroth and Toshiyuki Ohtsuka and Florian Weissel and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{F}inite-{H}orizon {O}ptimal {S}tate-{F}eedback {C}ontrol of {N}onlinear {S}tochastic {S}ystems {B}ased on a {M}inimum {P}rinciple},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {371--376},
  address   = {Heidelberg, Germany},
  month     = sep,
  abstract  = {In this paper, an approach to the finite-horizon
               optimal state-feedback control problem of nonlinear, stochastic,
               discrete-time systems is presented. Starting from the dynamic
               equation, the value function will be approximated
               by means of Taylor series expansion up to second-order
               derivatives. Moreover, the problem will be reformulated, such
               that a minimum principle can be applied to the stochastic
               problem. Employing this minimum principle, the optimal control
               problem can be rewritten as a two-point boundary-value
               problem to be solved at each time step of a shrinking horizon.
               To avoid numerical problems, the two-point boundary-value
               problem will be solved by means of a continuation method.
               Thus, the curse of dimensionality of dynamic programming
               is avoided, and good candidates for the optimal state-feedback
               controls are obtained. The proposed approach will be evaluated
               by means of a scalar example system.},
  pdf       = {MFI06_DeisenrothWeissel.pdf}
}

@inproceedings{MFI06_Huber,
  author    = {Marco Huber and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{C}losed-{F}orm {P}rediction of {N}onlinear {D}ynamic {S}ystems by {M}eans of {G}aussian {M}ixture {A}pproximation of the {T}ransition {D}ensity},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {98--103},
  address   = {Heidelberg, Germany},
  month     = sep,
  abstract  = {Recursive prediction of the state of a nonlinear stochastic dynamic
               system cannot be efficiently performed in general, since the complexity
               of the probability density function characterizing the system state
               increases with every prediction step. Thus, representing the density
               in an exact closed-form manner is too complex or even impossible.
               So, an appropriate approximation of the density is required. Instead
               of directly approximating the predicted density, we propose the approximation
               of the transition density by means of Gaussian mixtures. We treat
               the approximation task as an optimization problem that is solved
               offline via progressive processing to bypass initialization problems
               and to achieve high quality approximations. Once having calculated
               the transition density approximation offline, prediction can be performed
               efficiently resulting in a closed-form density representation with
               constant complexity.},
  keywords  = {System Approximation, Gaussian Mixtures},
  pdf       = {MFI06_Huber.pdf}
}

@inproceedings{MFI06_Schrempf-CramerMises,
  author    = {Oliver C. Schrempf and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Dirac Mixture Density Approximation Based on Minimization of the Weighted Cram\'{e}r-von Mises Distance}},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {512--517},
  address   = {Heidelberg, Germany},
  month     = sep,
  abstract  = {This paper proposes a systematic procedure for approximating arbitrary
               probability density functions by means of Dirac mixtures. For that
               purpose, a distance measure is required, which is in general not
               well defined for Dirac mixture densities. Hence, a distance measure
               comparing the corresponding cumulative distribution functions is
               employed. Here, we focus on the weighted Cramer-von Mises distance,
               a weighted integral quadratic distance measure, which is simple and
               intuitive. Since a closed-form solution of the given optimization
               problem is not possible in general, an efficient solution procedure
               based on a homotopy continuation approach is proposed. Compared to
               a standard particle approximation, the proposed procedure ensures
               an optimal approximation with respect to a given distance measure.
               Although useful in their own respect, the results also provide the
               basis for a recursive nonlinear filtering mechanism as an alternative
               to the popular particle filters.},
  keywords  = {Dirac Mixtures},
  pdf       = {MFI06_Schrempf-CramerMises.pdf}
}

@inproceedings{MFI06_Wang,
  author    = {Hui Wang and Henning Lenz and Andrei Szabo and Uwe D. Hanebeck and Joachim Bamberger},
  title     = {{F}usion of {B}arometric {S}ensors, {WLAN} {S}ignals and {B}uilding {I}nformation for 3--{D} {I}ndoor/{C}ampus {L}ocalization},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {426--432},
  address   = {Heidelberg, Germany},
  month     = sep,
  pdf       = {MFI06_Wang.pdf},
  abstract  = {Location estimation in indoor/campus
               environments has attracted much interest for its broad
               applications. Many applications (e.g. personnel security)
               require not only the 2-D coordinate but also the floor index
               where the mobile users are situated. However, most of the
               current location systems cannot provide the floor information
               accurately and robustly. In this paper, we propose a 3-D
               localization scheme which fuses the barometric sensor with
               Wireless LAN (WLAN) signals and building information. Our
               experiments show that this fusion scheme can both identify the
               floor index without errors and improve the horizontal
               localization accuracy. Moreover, since the barometric sensor is
               quite simple and cheap, it would bring almost no increase in
               system costs.}
}

@inproceedings{ICINCO06_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Timothy Armstrong and Oliver Hessel and Michael Mende and Uwe D. Hanebeck},
  title     = {{A} {N}ovel {H}aptic {I}nterface for {F}ree {L}ocomotion in {E}xtended {R}ange {T}elepresence {S}cenarios},
  booktitle = {Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)},
  year      = {2006},
  pages     = {148--153},
  address   = {Set\'{u}bal, Portugal},
  month     = aug,
  pdf       = {ICINCO06_Roessler.pdf},
  abstract  = {Telepresence gives a user the impression of actually
               being present in a distant environment. A mobile teleoperator acts
               as a proxy in this target environment, replicates the user's motion,
               and records sensory information, which is transferred to the user
               and displayed in real-time. As a result the user is immersed in the
               target environment. The user can then control the teleoperator by
               walking naturally. Motion Compression, a nonlinear mapping between
               the user's and the robot's motion, allows exploration of large
               target environments even from small user environments.
               For manipulation tasks haptic feedback is important. However, current
               haptic displays do not allow wide-area motion. In this work we present
               our design of a novel haptic display for simultaneous wide area motion
               and haptic interaction.}
}

@inproceedings{ICINCO06_SawoRoberts,
  author    = {Felix Sawo and Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{B}ayesian {E}stimation of {D}istributed {P}henomena using {D}iscretized {R}epresentations of {P}artial {D}ifferential {E}quations},
  booktitle = {Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)},
  year      = {2006},
  pages     = {16--23},
  address   = {Set\'{u}bal, Portugal},
  month     = aug,
  abstract  = {This paper addresses a systematic method for the reconstruction and
               the prediction of a distributed phenomenon characterized by partial
               differential equations, which is monitored by a sensor network. In
               the first step, the infinite-dimensional partial differential equation,
               i.e. distributed-parameter system, is spatially and temporally decomposed
               leading to a finite-dimensional state space form. In the next step,
               the state of the resulting lumped-parameter system, which provides
               an approximation of the solution of the underlying partial differential
               equations, is dynamically estimated under consideration of uncertainties
               both occurring in the system and arising from noisy measurements.
               By using the estimation results, several additional tasks can be
               achieved by the sensor network, e.g. optimal sensor placement, optimal
               scheduling, and model improvement. The performance of the proposed
               model-based reconstruction method is demonstrated by means of simulations.},
  pdf       = {ICINCO06_SawoRoberts.pdf}
}

@inproceedings{Fusion06_Klumpp,
  author    = {Vesa Klumpp and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{A}pproximate {N}onlinear {B}ayesian {E}stimation {B}ased on {L}ower and {U}pper {D}ensities},
  booktitle = {Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)},
  year      = {2006},
  address   = {Florence, Italy},
  month     = jul,
  abstract  = {Recursive calculation of the probability density function characterizing
               the state estimate of a nonlinear stochastic dynamic system in general
               cannot be performed exactly, since the type of the density changes
               with every processing step and the complexity increases. Hence, an
               approximation of the true density is required. Instead of using a
               single complicated approximating density, this paper is concerned
               with bounding the true density from below and from above by means
               of two simple densities. This provides a kind of guaranteed estimator
               with respect to the underlying true density, which requires a mechanism
               for ordering densities. Here, a partial ordering with respect to
               the cumulative distributions is employed. Based on this partial ordering,
               a modified Bayesian filter step is proposed, which recursively propagates
               lower and upper density bounds. A specific implementation for piecewise
               linear densities with finite support is used for demonstrating the
               performance of the new approach in simulations.},
  comment   = {Dieses Paper behandelt die Ideen zu Behandlung von Mengen von Wahrscheinlichkeitsdichten.
               Es entstand im Rahmen der Diplomarbeit von V.Klumpp.},
  pdf       = {Fusion06_Klumpp.pdf}
}

@inproceedings{Fusion06_Sawo-ParameterizedGMs,
  author    = {Felix Sawo and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{P}arameterized {J}oint {D}ensities with {G}aussian and {G}aussian {M}ixture {M}arginals},
  booktitle = {Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)},
  year      = {2006},
  address   = {Florence, Italy},
  month     = jul,
  abstract  = {In this paper we attempt to lay the foundation for a novel filtering
               technique for the fusion of two random vectors with imprecisely known
               stochastic dependency. This problem mainly occurs in decentralized
               estimation, e.g., of a distributed phenomenon, where the stochastic
               dependencies between the individual states are not stored. Thus,
               we derive parameterized joint densities with both Gaussian marginals
               and Gaussian mixture marginals. These parameterized joint densities
               contain all information about the stochastic dependencies between
               their marginal densities in terms of a parameter vector xi, which
               can be regarded as a generalized correlation parameter. Unlike the
               classical correlation coeffcient, this parameter is a suffcient measure
               for the stochastic dependency even characterized by more complex
               density functions such as Gaussian mixtures. Once this structure
               and the bounds of these parameters are known, bounding densities
               containing all possible density functions could be found.},
  pdf       = {Fusion06_Sawo-ParameterizedGMs.pdf}
}

@inproceedings{Fusion06_SchrempfHanselmann,
  author    = {Oliver C. Schrempf and Anne Hanselmann and Uwe D. Hanebeck},
  title     = {{E}fficient {R}epresentation and {F}usion of {H}ybrid {J}oint {D}ensities for {C}lusters in {N}onlinear {H}ybrid {B}ayesian {N}etworks},
  booktitle = {Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)},
  year      = {2006},
  address   = {Florence, Italy},
  month     = jul,
  abstract  = {Undirected cycles in Bayesian networks are often treated by using
               clustering methods. This results in networks with nodes characterized
               by joint probability densities instead of marginal densities. An
               efficient representation of these hybrid joint densities is essential
               especially in nonlinear hybrid networks containing continuous as
               well as discrete variables. In this article we present a unified
               representation of continuous, discrete, and hybrid joint densities.
               This representation is based on Gaussian and Dirac mixtures and allows
               for analytic evaluation of arbitrary hybrid networks without loosing
               structural information, even for networks containing clusters. Furthermore
               we derive update formulae for marginal and joint densities from a
               system theoretic point of view by treating a Bayesian network as
               a system of cascaded subsystems. Together with the presented mixture
               representation of densities this yields an exact analytic updating
               scheme.},
  keywords  = {Hybrid Bayesian Networks},
  pdf       = {Fusion06_SchrempfHanselmann.pdf}
}

@proceedings{DBLP:conf/dagstuhl/2005P5381,
  editor    = {Leonidas J. Guibas and
               Uwe D. Hanebeck and
               Thomas C. Henderson},
  title     = {Form and Content in Sensor Networks, 18.09. - 23.09.2005},
  booktitle = {Form and Content in Sensor Networks},
  publisher = {Internationales Begegnungs- und Forschungszentrum f{\"u}r
               Informatik (IBFI), Schloss Dagstuhl, Germany},
  address   = {Internationales Begegnungs- und Forschungszentrum f{\"u}r
               Informatik (IBFI), Schloss Dagstuhl, Germany},
  series    = {Dagstuhl Seminar Proceedings},
  volume    = {05381},
  month     = sep,
  year      = {2006},
  url       = {https://drops.dagstuhl.de/portals/05381/},
  bibsource = {DBLP, https://dblp.uni-trier.de},
  abstract  = {From 18.09.05 to 23.09.05, the Dagstuhl Seminar 05381 Form and Content in Sensor Networks was held in
               the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants
               presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given
               during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section
               describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.},
  pdf       = {05381_abstracts_collection.755.pdf}
}

@inproceedings{ECC05_Rauh,
  author    = {Andreas Rauh and Uwe D. Hanebeck},
  title     = {{Moment--Based Prediction Step for Nonlinear Discrete-Time Dynamic Systems Using Exponential Densities}},
  booktitle = {Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005)},
  year      = {2005},
  address   = {Sevilla, Spain},
  month     = dec,
  pdf       = {ECC05_Rauh.pdf},
  abstract  = {In this paper, an efficient approach for a momentbased
               Bayesian prediction step for both linear and nonlinear
               discrete-time dynamic systems using exponential densities with
               polynomial exponents is proposed. The exact solution of the
               prediction step is approximated by an exponential density
               which minimizes the Kullback-Leibler distance. Compared to
               other approaches, the user of this procedure can specify the
               approximation quality by controlling the deviation between
               the moments of the exact and the approximated solution.
               Furthermore, this algorithm can also be used for the adaptation
               of the order of the exponential densities either to improve the
               approximation quality or to reduce the computational effort.}
}

@inproceedings{INTETAIN05_Groenda,
  author    = {Henning Groenda and Fabian Nowak and Patrick R{\"o}\ss{}ler and Uwe D. Hanebeck},
  title     = {{T}elepresence {T}echniques for {C}ontrolling {A}vatar {M}otion in {F}irst {P}erson {G}ames},
  booktitle = {Intelligent Technologies for Interactive Entertainment (INTETAIN 2005)},
  year      = {2005},
  address   = {Madonna di Campiglio, Italy},
  month     = nov,
  pdf       = {INTETAIN05_Groenda.pdf},
  abstract  = {First person games are computer games, in which the user
               experiences the virtual game world from an avatar's view. This avatar is
               the user's alter ego in the game. In this paper, we present a telepresence
               interface for the first person game Quake III Arena, which gives the user
               the impression of presence in the game and thus leads to identification
               with his avatar. This is achieved by tracking the user's motion and using
               this motion data as control input for the avatar. As the user is wearing a
               head-mounted display and he perceives his actions affecting the virtual
               environment, he fully immerses into the target environment. Without
               further processing of the user's motion data, the virtual environment
               would be limited to the size of the user's real environment, which is not
               desirable. The use of Motion Compression, however, allows exploring an
               arbitrarily large virtual environment while the user is actually moving in
               an environment of limited size.}
}

@incollection{AMS05_RobertsSzabo,
  author    = {Kathrin Roberts and G{\'{a}}bor Szab{\'{o}} and Uwe D. Hanebeck},
  title     = {{S}ensorgest{\"u}tzte {B}ewegungssynchronisation von {O}perationsinstrumenten am schlagenden {H}erzen},
  booktitle = {Autonome Mobile Systeme 2005 (AMS 2005), 19.~Fachgespr{\"a}ch, Stuttgart, Informatik Aktuell},
  publisher = {Springer},
  year      = {2005},
  editor    = {P. Levi and M. Schanz and R. Lafrenz and V. Avrutin},
  pages     = {269-275},
  month     = oct,
  abstract  = {Offene oder minimal invasive Operationen am schlagenden Herzen erfordern
               von dem Chirurgen eine hohe Konzentrationsf{\"a}higkeit {\"u}ber
               einen l{\"a}ngeren Zeitraum. Daher ist es f{\"u}r den Chirurgen sehr
               hilfreich durch ein robotergest{\"u}tztes Chirurgiesystem unterst{\"u}tzt
               zu werden, das die Instrumente im Interventionsareal mit der Herzbewegung
               synchronisiert. Um eine Bewegungskompensation durchzuf{\"u}hren,
               muss ein Mechanismus gefunden werden, der aufgrund einer Pr{\"a}diktion
               der Herzbewegung die Instrumente nachf{\"u}hrt. F{\"u}r die Pr{\"a}diktion
               der Herzbewegung wird in diesem Artikel ein Verfahren zum Entwurf
               eines stochastischen 3D-Bewegungsmodells f{\"u}r die Herzoberfl{\"a}che
               gezeigt. Ein Sch{\"a}tzer nimmt dieses Modell als Grundlage und verwendet
               die verrauschten Sensormessungen von Landmarken der Herzoberfl{\"a}che
               um die Herzoberfl{\"a}chenbewegung zu pr{\"a}dizieren.},
  pdf       = {InfoAktuell05_RobertsSzabo.pdf}
}

@article{AT05_Briechle,
  author    = {Kai Briechle and Uwe D. Hanebeck},
  title     = {{L}okalisierung mittels mengenbasierter nichtlinearer {F}ilterung im {H}yperraum},
  journal   = {at -- Automatisierungstechnik},
  year      = {2005},
  volume    = {53},
  pages     = {415--424},
  month     = sep,
  publisher = {Oldenbourg Verlag},
  url       = {https://dx.doi.org/10.1524/auto.2005.53.9.415},
  abstract  = {In recent years methods for localization of objects
               have found widespread use as the basis for numerous novel technical
               applications. The majority of these methods are based on filtering
               algorithms which are used to estimate the position and orientation of
               the object that shall be localized. In this article, a novel efficient
               nonlinear filtering concept is presented. It is well suited for localization
               problems for which conventional filtering algorithms based on linearization
               do not yield satisfactory results. The performance of the proposed
               algorithm is demonstrated by applications to prototypical localization
               problems and an experiment, in which a mobile robot is localized.}
}

@inproceedings{CLAWAR05_GassmannHuber,
  author    = {Bernd Ga\ss{}mann and Marco Huber and J. Marius Z{\"o}llner and R{\"u}diger Dillmann},
  title     = {{Navigation of Walking Robots: Path Planning}},
  booktitle = {Proceedings of the 8th International Conference on Climbing and Walking Robots (CLAWAR 2005)},
  year      = {2005},
  volume    = {1},
  pages     = {115--122},
  address   = {London, United Kingdom},
  month     = sep,
  abstract  = {Proper navigation of walking machines in unstructured terrain requires
               a path planning algorithm that reflects the flexibility of the robots
               movements. This paper discusses the problem and presents a real time
               capable path planning algorithm for walking machines that also makes
               use of their climbing abilities.}
}

@inproceedings{CSIT05_Weissel,
  author    = {Florian Weissel and Uwe D. Hanebeck},
  title     = {{A} {T}est-{E}nvironment for {C}ontrol {S}chemes in the {F}ield of {C}ollaborative {R}obots and {S}warm {I}ntelligence},
  booktitle = {Proceedings of the 7th International Workshop on Computer Science and Information Technologies (CSIT 2005)},
  year      = {2005},
  volume    = {1},
  address   = {Ufa, Russian Federation},
  month     = sep,
  abstract  = {This paper presents an architecture for a test-environment for algorithms
               and control schems in the filed of collaborative robotics and swarm
               intelligence. As the foundation of the test-environment, small bionically
               inspired robots are presented. The robots are small (20 cm x 5 cm
               x 5 cm) and lightweight (< 200g). Their design is inspired by the
               movement of caterpillars. Threee cubical segments are connected via
               special joints, where each of these joints has three independent
               degrees of translatory freedom. Thus, the robots are able to handle
               rough terrain with small obstacle. The robots are driven by innovative
               piezoelectric motors that allow a gearless design without any rotary
               parts. Each robot is equipped with on-board processing and radio
               communication. The software of the robots is written using TinyOS,
               an event-driven operating system for large-scale distributed sensor-actuator-networks.},
  pdf       = {CSIT05_Weissel.pdf}
}

@inproceedings{ICINCO05_Hahn,
  author    = {Daniel Hahn and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Visual Scene Augmentation for Enhanced Human Perception}},
  booktitle = {Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)},
  year      = {2005},
  volume    = {2},
  pages     = {146--153},
  address   = {Barcelona, Spain},
  month     = sep,
  abstract  = {In this paper we present an assistive system for hearing-impaired
               people that consists of a wearable microphone array and an Augmented
               Reality (AR) system. This system helps the user in communication
               situations, where many speakers or sources of background noise are
               present. In order to restore the cocktail party effect multiple
               microphones are used to estimate the position of individual sound
               sources. In order to allow the user to interact in complex situations
               with many speakers, an algorithm for estimating the user's attention
               is developed. This algorithm determines the sound sources, which
               are in the user's focus of attention. It allows the system to discard
               irrelevant information and enables the user to focus on certain aspects
               of the surroundings. Based on the user's hearing impairment, the
               perception of the speaker in the focus of attention can be enhanced,
               e.g. by amplification or using a speech-to-text conversion. A prototype
               has been built for evaluating this approach. Currently the prototype
               is able to locate sound beacons in three-dimensional space, to perform
               a simple focus estimation, and to present floating captions in the
               Augmented Reality. The prototype uses an intentionally simple user
               interface, in order to minimize distractions.},
  pdf       = {ICINCO05_Hahn.pdf}
}

@inproceedings{ICINCO05_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{A} {F}ramework for {T}elepresent {G}ame-{P}lay in {L}arge {V}irtual {E}nvironments},
  booktitle = {Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)},
  year      = {2005},
  volume    = {3},
  pages     = {150--155},
  address   = {Barcelona, Spain},
  month     = sep,
  abstract  = {In this paper we present a framework that provides a novel interface
               to avatar control in immersive computer games. The user's motion
               is tracked and transferred to to the game environment. This motion
               data is used as control input for the avatar. The game graphics are
               rendered according to the avatar's motion and presented to the user
               on a head-mounted display. As a result, the user immerses into the
               game environment and identifies with the avatar. However, without
               further processing of the motion data, the virtual environment would
               be limited to the size of the user's real environment, which is not
               desirable. By using Motion Compression, the framework allows exploring
               an arbitrarily large virtual environment while the user is actually
               moving in an environment of limited size. Based on the proposed framework,
               two game applications were implemented, a modification of a commercially
               available game and a custom designed game. These two applications
               prove, that a telepresence system using Motion Compression is a highly
               intuitive interface to game control.},
  pdf       = {ICINCO05_Roessler.pdf}
}

@inproceedings{ICINCO05_Schrempf-Intention,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{A} {G}eneric {M}odel for {E}stimating {U}ser {I}ntentions in {H}uman-{R}obot {C}ooperation},
  booktitle = {Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)},
  year      = {2005},
  volume    = {3},
  pages     = {251--256},
  address   = {Barcelona, Spain},
  month     = sep,
  abstract  = {The recognition of user intentions is an important feature for humanoid
               robots to make implicit and human-like interactions possible. In
               this paper, we introduce a formal view on user-intentions in human-machine
               interaction and how they can be estimated by observing user actions.
               We use Hybrid Dynamic Bayesian Networks to develop a generic model
               that includes connections between intentions, actions, and sensor
               measurements. This model can be used to extend arbitrary human-machine
               applications by intention recognition.},
  keywords  = {Intention Recognition},
  pdf       = {ICINCO05_Schrempf-Intention.pdf}
}

@inproceedings{CCA05_Sawo,
  author    = {Felix Sawo and Masayuki Fujita and Oliver Sawodny},
  title     = {{P}assivity--{B}ased {D}ynamic {V}isual {F}eedback {C}ontrol of {M}anipulators with {K}inematic {R}edundancy},
  booktitle = {Proceedings of the 2005 IEEE Conference on Control Applications (CCA 2005)},
  year      = {2005},
  pages     = {1200--1205},
  month     = aug,
  doi       = {10.1109/CCA.2005.1507294},
  pdf       = {CCA05_Sawo.pdf}
}

@inproceedings{IROS05_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Frederik Beutler and Uwe D. Hanebeck and Norbert Nitzsche},
  title     = {{M}otion {C}ompression {A}pplied to {G}uidance of a {M}obile {T}eleoperator},
  booktitle = {Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems (IROS 2005)},
  year      = {2005},
  pages     = {2495--2500},
  address   = {Edmonton, Canada},
  month     = aug,
  abstract  = {Telepresence aims at giving a human user the impression of being present
               in a remote environment. However, the user is actually situated in
               a user environment and his motion is tracked. A mobile teleoperator
               in the remote environment replicates this motion. The user can thus
               control the mobile teleoperator's locomotion by walking. A stereo-camera
               system mounted on the mobile teleoperator constantly records live
               camera images and transfers them to the user environment, where they
               are presented to the user on a head-mounted display. This paper presents
               a long distance experiment, in which a mobile teleoperator was controlled
               over a standard internet connection by natural locomotion. Without
               further processing of the user's motion data, however, only exploration
               of a remote environment of the same size or smaller than the user
               environment is possible. As this is not desirable, we use Motion
               Compression, an optimal nonlinear transformation of the user's path.
               This algorithm allows controlling free motion in an arbitrarily large
               target environment from a limited user environment.},
  pdf       = {IROS05_Roessler.pdf}
}

@inproceedings{ROMAN05_SchrempfSchmid,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck and Andreas J. Schmid. and Heinz W{\"o}rn},
  title     = {{A} {N}ovel {A}pproach to {P}roactive {H}uman-{R}obot {C}ooperation},
  booktitle = {Proceedings of the 2005 IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005)},
  year      = {2005},
  pages     = {555--560},
  address   = {Nashville, Tennessee, USA},
  month     = aug,
  abstract  = {This paper introduces the concept of proactive execution of robot
               tasks in the context of human-robot cooperation with uncertain knowledge
               of the human's intentions. We present a system architecture that
               defines the necessary modules of the robot and their interactions
               with each other. The two key modules are the intention recognition
               that determines the human user's intentions and the planner that
               executes the appropriate tasks based on those intentions. We show
               how planning conflicts due to the uncertainty of the intention information
               are resolved by proactive execution of the corresponding task that
               optimally reduces the system's uncertainly. Finally, we present an
               algorithm for selecting this task and suggest a benchmark scenario.},
  keywords  = {collision avoidance, humanoid robots, man-machine systems, mobile
               robots, human centered computing, humanoid robots, intention recognition,
               proactive human-robot cooperation, robot tasks},
  pdf       = {ROMAN05_SchrempfSchmid.pdf}
}

@article{AT05_Grigoras,
  author    = {Marian Grigoras and Olga Feiermann and Uwe D. Hanebeck},
  title     = {{D}ata-{D}riven {M}odeling of {S}ignal {S}trength {D}istributions for {L}ocalization in {C}ellular {R}adio {N}etworks ({D}atengetriebene {M}odellierung von {F}eldst{\"a}rkeverteilungen f{\"u}r die {O}rtung in zellul{\"a}ren {F}unknetzen)},
  journal   = {at -- Automatisierungstechnik, Sonderheft: Datenfusion in der Automatisierungstechnik},
  year      = {2005},
  volume    = {53},
  pages     = {314--321},
  number    = {7},
  month     = jul,
  publisher = {Oldenbourg Verlag},
  abstract  = {In this article, we propose a novel approach to solving
               the localization problem in cellular networks, with a focus on indoor environments.
               The goal is to estimate a mobile user's position, based on measurements of signals
               received from different base stations. Our solution uses the Progressive
               Bayes estimation framework to model the distribution of signal measurements,
               as obtained in a series of calibration measurements. In the localization step,
               we compute the full probability density over the user position. We also show
               that user motion models can be easily integrated in our solution.},
  url       = {https://dx.doi.org/10.1524/auto.53.7.314.66708}
}

@book{AT05_Hanebeck,
  title     = {at -- {A}utomatisierungstechnik, {S}onderheft: {D}atenfusion in der {A}utomatisierungstechnik},
  publisher = {Oldenbourg Verlag},
  year      = {2005},
  editor    = {Uwe D. Hanebeck and Hartwig Steusloff},
  volume    = {53},
  number    = {7},
  series    = {at -- Automatisierungstechnik},
  month     = jul,
  pdf       = {AT05_Hanebeck.pdf}
}

@inproceedings{Fusion05_Roberts,
  author    = {Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{P}rediction and {R}econstruction of {D}istributed {D}ynamic {P}henomena {C}haracterized by {L}inear {P}artial {D}ifferential {E}quations},
  booktitle = {Proceedings of the 8th International Conference on Information Fusion (Fusion 2005)},
  year      = {2005},
  address   = {Philadelphia, Pennsylvania, USA},
  month     = jul,
  abstract  = {A primary challenge for the reconstruction of continuous-time, continuous-amplitude
               distributed parameter systems is the inclusion of recent discrete-time,
               discreteamplitude, spatially discrete measurements. Hence, a systematic
               method for data processing is required that also handles incomplete
               and noisy data, e.g. data from a sensor network. This article presents
               two approaches to the reconstruction of distributed parameter systems
               that can be described by linear partial differential equations (PDEs)
               and involve one or several discrete measurement points. In both approaches,
               the linear PDE is first converted into a bank of linear lumped systems
               by means of modal analysis. In addition, a measurement equation relating
               state and (sensor) data is derived. In the second step, a Kalman
               filter (KF) is used to dynamically estimate the state of the lumped
               systems, which provides an approximation of the solution of the underlying
               PDE. The first approach uses Fourier Analysis. The second approach
               uses Fourier Analysis and the collocation method. The approaches
               are both demonstrated for a simple linear inhomogeneous PDE, the
               one-dimensional heat equation.},
  pdf       = {Fusion05_Roberts.pdf}
}

@inproceedings{Fusion05_Schrempf-MixtureReduction,
  author    = {Oliver C. Schrempf and O. Feiermann and Uwe D. Hanebeck},
  title     = {{O}ptimal {M}ixture {A}pproximation of the {P}roduct of {M}ixtures},
  booktitle = {Proceedings of the 8th International Conference on Information Fusion (Fusion 2005)},
  year      = {2005},
  volume    = {1},
  pages     = {85--92},
  address   = {Philadelphia, Pennsylvania, USA},
  month     = jul,
  abstract  = {Gaussian mixture densities are very common today to describe arbitrary
               structured uncertainties in various applications. Many of these applications
               have to deal with the fusion of uncertainties, an operation that
               is usually performed by multiplication of these densities. The product
               of Gaussian mixtures can be calculated exactly, but the number of
               mixture components in the resulting mixture increases in an exponential
               way. Hence, it is essential to approximate this resulting mixture
               with less components, to keep it tractable for further processing
               steps. This paper introduces an approach to approximate the exact
               product with a mixture that uses less components. The maximum approximation
               error can be chosen by the user. This choice allows to trade accuracy
               of the approximation for the number of mixture components used. This
               is possible due to the usage of a progressive processing scheme that
               calculates the product operation by means of a system of ordinary
               differential equations. The solution of this system yields the parameters
               of the desired Gaussian mixture.},
  keywords  = {Gaussian Mixtures},
  pdf       = {Fusion05_Schrempf-MixtureReduction.pdf}
}

@inproceedings{IFAC05_Schrempf-HybridBN,
  author       = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title        = {{E}valuation of {H}ybrid {B}ayesian {N}etworks using {A}nalytical {D}ensity {R}epresentations},
  booktitle    = {Proceedings of the 16th IFAC World Congress (IFAC 2005)},
  year         = {2005},
  address      = {Prague, Czech Republic},
  month        = jul,
  organization = {IFAC},
  abstract     = {In this article, a new mechanism is described for modeling and evaluating
                  Hybrid Dynamic Bayesian networks. The approach uses Gaussian mixtures
                  and Dirac mixtures as messages to calculate marginal densities. As
                  these densities are approximated by means of Gaussian mixtures, any
                  desired precision is possible. The presented approach removes the
                  restrictions of sample based evaluation of Bayesian networks since
                  it uses an analytical approximation scheme for probability densities
                  which systematically minimizes the distance between the exact and
                  the approximate density.},
  keywords     = {Hybrid Bayesian Networks},
  pdf          = {IFAC05_Schrempf-HybridBN.pdf}
}

@inproceedings{ICRA05_Brunn-Splines,
  author    = {Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{A} {M}odel-{B}ased {F}ramework for {O}ptimal {M}easurements in {M}achine {T}ool {C}alibration},
  booktitle = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005)},
  year      = {2005},
  address   = {Barcelona, Spain},
  month     = apr,
  abstract  = {Calibration is the procedure of quantifying mechanical depciencies
               of machines and compensating them by appropriate adjustment. This
               paper introduces a model- based measurement framework for improving
               calibration procedures of machine tools. The goal is to precisely
               estimate the mechanical depciencies based on a minimal number of
               measurements. For that purpose, the mechanical depciencies of linear
               and rotary joints are modeled using splines. Uncertainties of the
               depciency model are formulated stochastically, which allows incorporation
               of imprecise measurement data and prediction of optimal measurement
               parameters. We derive a method for optimally estimating a set of
               splines, i.e., joint errors, based on a set of measurements and for
               predicting the optimal joint configuration for new measurements.},
  pdf       = {ICRA05_Brunn-Splines.pdf}
}

@inproceedings{ICASSP05_Beutler,
  author    = {Frederik Beutler and Uwe D. Hanebeck},
  title     = {{C}losed-{F}orm {R}ange-{B}ased {P}osture {E}stimation {B}ased on {D}ecoupling {T}ranslation and {O}rientation},
  booktitle = {Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005)},
  year      = {2005},
  volume    = {4},
  pages     = {989--992},
  address   = {Philadelphia, Pennsylvania, USA},
  month     = mar,
  abstract  = {For estimating the posture, i.e., position and orientation, of an
               extended target based on range measurements, a new closed-form solution
               is proposed, which is based on decoupling position and orientation.
               For decoupling, any procedure for range-based localization of point
               targets, i.e., for mere position estimation, can be used. The new
               solution is suboptimal, but nevertheless provides good accuracy and
               is very practical from an application point of view.},
  pdf       = {ICASSP05_Beutler.pdf}
}

@inproceedings{CSIT04-Schrempf_HybridBN,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{A} {N}ew {A}pproach for {H}ybrid {B}ayesian {N}etworks {U}sing {F}ull {D}ensities},
  booktitle = {Proceedings of the 6th International Workshop on Computer Science and Information Technologies (CSIT 2004)},
  year      = {2004},
  volume    = {1},
  pages     = {32--37},
  address   = {Budapest, Hungary},
  month     = oct,
  abstract  = {In this article, a new mechanism is described for modeling and evaluating
               hybrid Bayesian networks. The approach uses Gaussian mixtures and
               Dirac mixtures as messages to calculate marginal densities. The mechanism
               is proven to be exact, hence the accuracy of resulting marginals
               is only dependending on the accuracy of the conditional densities.
               As these densities are approximated by means of Gaussian mixtures,
               any desired precision can be achieved. The presented approach removes
               the restrictions concerning the ancestry of discrete nodes often
               made in literature. Hence it enables the designer to model arbitrary
               parent-child relationships using continuous and discrete variables.},
  keywords  = {Hybrid Bayesian Networks},
  pdf       = {CSIT04-Schrempf_HybridBN.pdf}
}

@inproceedings{Sensors04_Beutler,
  author    = {Frederik Beutler and Uwe D. Hanebeck},
  title     = {{A} {N}ew {N}onlinear {F}iltering {T}echnique for {S}ource {L}ocalization},
  booktitle = {Proceedings of the 3rd IEEE Conference on Sensors (Sensors 2004)},
  year      = {2004},
  volume    = {1},
  pages     = {413--416},
  address   = {Vienna, Austria},
  month     = oct,
  abstract  = {A new model-based approach for estimating the parameters of an arbitrary
               transformation between two discrete-time sequences will be introduced.
               One sequence is interpreted as part of a nonlinear measurement equation,
               the other sequence is typically measured sequentially. Based on every
               measured value, the probability density function of the parameters
               is updated using a Bayesian approach. For the evolution of the system
               over time, a system equation is included. The new approach provides
               a high update rate for the desired parameters up to the sampling
               rate with high accuracy. It will be demonstrated for source localization
               of a speaker, where the parameters describe the position of the source.},
  pdf       = {Sensors04_Beutler.pdf}
}

@inproceedings{SMC04_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Uwe D. Hanebeck},
  title     = {{T}elepresence {T}echniques for {E}xception {H}andling in {H}ousehold {R}obots},
  booktitle = {Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004)},
  year      = {2004},
  volume    = {1},
  pages     = {53--58},
  address   = {The Hague, The Netherlands},
  month     = oct,
  doi       = {10.1109/ICSMC.2004.1398272},
  abstract  = {The hardware for humanoid household robots
               will be available in the near future. As households are unstructured
               domains and interaction with human users is ambiguous,
               these robots encounter unexpected situations, so
               called exceptions. These render the robot unusable, if not
               handled fast and transparently to the user. Telepresent exception
               handling gives the technical staff an intuitive interface
               to the robots and is transparent to the client. We propose
               a service center for telepresent exception handling, which
               is directly available and thus allows the use of humanoid
               domestic robots as soon as the corresponding hardware is
               commercially available.},
  pdf       = {SMC04_Roessler.pdf}
}

@inproceedings{MECHROB04_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Uwe D. Hanebeck and Norbert Nitzsche},
  title     = {{F}eedback {C}ontrolled {M}otion {C}ompression for {E}xtended {R}ange {T}elepresence},
  booktitle = {Proceedings of the 2004 IEEE International Conference on Mechatronics \& Robotics (MechRob 2004)},
  year      = {2004},
  pages     = {1447-1452},
  address   = {Aachen, Germany},
  month     = sep,
  abstract  = {Telepresence aims at giving a human user the
               impression of being present in a remote environment. This
               is achieved by having a robot, that is actually present in the
               remote environment, gather visual data. This data is presented
               to the user, who is wearing a head mounted display. In order
               to extend telepresence to an intuitive user interface for robot
               teleoperation, the user's motion is tracked and replicated by
               the robot. The user can now interact more naturally with
               the remote environment simply by walking around. Without
               further processing of the motion data, the size of the remote
               environment is limited to the size of the user environment.
               Using the motion compression algorithm allows users to be
               telepresent in large target environments while the size of the
               user environment is limited. However, as a consequence of
               the influence of standard motion compression on the user's
               natural navigation, he tends to leave the desired path. This can
               even lead to users leaving the user environment. We address
               this problem by introducing controlled motion compression
               which adds a feedback controller to motion compression.
               This controller modifies the user's perception of the target
               environment in such a way, that he is controlled on the path.},
  pdf       = {MECHROB04_Roessler.pdf}
}

@inproceedings{IPDPS04_Weissel,
  author    = {Michael Huebner and Michael Ullmann and Florian Weissel and Juergen Becker},
  title     = {{Real-time Configuration Code Decompression for Dynamic FPGA Self-Reconfiguration}},
  booktitle = {Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS'04)},
  year      = {2004},
  pages     = {138--143},
  address   = {Santa F{\'e}, New Mexico, USA},
  month     = apr,
  abstract  = {Xilinx Virtex FPGAs have the possibility of dynamical partial run-time
               reconfiguration. If a system uses this feature with many different
               configuration bitstreams for substitution of parts in reconfiguration
               memory, the amout of neccesary memory increases. The sum of memory
               amout which has to be provided for the configuration data is not
               negligible. This fact suggests the investigation of compressing data
               before they are stored in memory modules of a system.The compressed
               bitstream data has to be decrompressed before transferring it to
               the FPGA. This paper shows an approach of compressing configuration
               data at design time and decompressing them with a hardware module
               implemented on FPGA while run-time.}
}

@article{PRE04_Nitzsche,
  author    = {Norbert Nitzsche and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}otion {C}ompression for {T}elepresent {W}alking in {L}arge {T}arget {E}nvironments},
  journal   = {Presence: Teleoperators \& Virtual Environments},
  year      = {2004},
  volume    = {13},
  pages     = {44--60},
  number    = {1},
  month     = feb,
  publisher = {The MIT press},
  abstract  = {Telepresent walking allows visits to remote places such as museums,
               exhibitions, architecture, or industrial sites with a high degree of realism.
               While walking freely around in the user environment, the user sees the
               remote environment through the eyes of a remote mobile teleoperator.
               For that purpose, the user's motion is tracked and transferred to the
               teleoperator. Without additional processing of the motion data, the size of
               the remote environment to be explored is limited to the size of the user environment.
               This paper proposes an extension of telepresent walking to arbitrarily large remote
               or virtual spaces based on compressing wide-area motion into the available user space.
               Motion compression is a novel approach and does not make use of scaling or
               walking-in-place metaphors. Rather, motion compression introduces some deviation of
               curvature between user motion and teleoperator motion. An optimization approach is
               used to find the user path of minimum curvature deviation with respect to a given
               predicted teleoperator path that fits inside the boundaries of the user environment.
               Turning angles and travel distances are mapped with a 1:1 ratio to provide the desired
               impression of realistic self-locomotion in the teleoperator's environment.
               The effects of the curvature deviation on inconsistent perception of
               locomotion are studied in two experiments.},
  url       = {https://dx.doi.org/10.1162/105474604774048225}
}

@article{RA04_Briechle,
  author   = {Kai Briechle and Uwe D. Hanebeck},
  title    = {{L}ocalization of a {M}obile {R}obot {U}sing {R}elative {B}earing {M}easurements},
  journal  = {IEEE Transactions on Robotics and Automation},
  year     = {2004},
  volume   = {20},
  pages    = {36--44},
  month    = feb,
  doi      = {https://dx.doi.org/10.1109/TRA.2003.820933},
  abstract = {In this paper, the problem of recursive robot localization
              based on relative bearing measurements is considered,
              where unknown but bounded measurement uncertainties are
              assumed. A common approach is to approximate the resulting
              set of feasible states by simple-shaped bounding sets such as, e.g.,
              axis-aligned boxes, and calculate the optimal parameters of this
              approximation based on the measurements and prior knowledge.
              In the novel approach presented here, a nonlinear transformation
              of the measurement equation into a higher dimensional space is
              performed. This yields a tight, possibly complex-shaped, bounding
              set in a closed-form representation whose parameters can be
              determined analytically for the measurement step. It is shown that
              the new bound is superior to commonly used outer bounds.},
  pdf      = {RA04_Briechle.pdf}
}

@inproceedings{CDC03_Feiermann-ProgBayes,
  author    = {Uwe D. Hanebeck and Olga Feiermann},
  title     = {{P}rogressive {B}ayesian {E}stimation for {N}onlinear {D}iscrete-{T}ime {S}ystems: {T}he {F}ilter {S}tep for {S}calar {M}easurements and {M}ultidimensional {S}tates},
  booktitle = {Proceedings of the 2003 IEEE Conference on Decision and Control (CDC 2003)},
  year      = {2003},
  pages     = {5366--5371},
  address   = {Maui, Hawaii, USA},
  month     = dec,
  abstract  = {This paper is concerned with recursively estimating the internal state
               sequence of a discrete-time dynamic system by processing a sequence
               of noisy measurements taken from the system output. Recursive processing
               requires some kind of sufficient statistic for representing the information
               collected up to a certain time step. For this purpose, the probability
               density functions of the state are especially well suited. Once they
               are available, almost any type of point estimate, e.g. mean, mode,
               or median, can be derived. In the case of continuous states, however,
               the exact probability density functions characterizing the state
               estimate are in general either not feasible or not well suited for
               recursive processing. Hence, approximations of the true densities
               are generally inevitable, where Gaussian mixture approximations are
               convenient for a number of reasons. However, calculating appropriate
               mixture parameters that minimize a global measure of deviation from
               the true density is a tough optimization task. Here, we propose a
               new approximation method that minimizes the squared integral deviation
               between the true density and its mixture approximation. Rather than
               trying to solve the original problem, it is converted into a corresponding
               system of explicit ordinary first-order differential equations. This
               system of differential equations is then solved over a finite time
               interval, which is an efficient way of calculating the desired optimal
               parameter values. We focus on the measurement update in the important
               case of vector states and scalar measurements. In addition, approximation
               densities with separable kernels are assumed. It will be shown, that
               if the measurement nonlinearities are also separable, the required
               multidimensional integrals can be reduced to the product of one-dimensional
               integrals. For several important types of measurement functions including
               polynomial measurement nonlinearities, closed-form analytic expressions
               for the coefficients of the system of differential equations are
               available.},
  pdf       = {CDC03_Feiermann-ProgBayes.pdf}
}

@incollection{AMS03_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Uwe D. Hanebeck and Marian Grigoras and Paul T. Pilgram and Joachim Bamberger and Clemens Hoffmann},
  title     = {{A}utomatische {K}artographierung der {S}ignalcharakteristik in {F}unknetzwerken},
  booktitle = {Autonome Mobile Systeme 2003 (AMS 2003), 18.~Fachgespr{\"a}ch, Karlsruhe, Informatik Aktuell},
  publisher = {Springer},
  year      = {2003},
  editor    = {R{\"u}diger Dillmann and Tilo Gockel and Heinz W{\"o}rn},
  url       = {https://dx.doi.org/10.1007/978-3-642-18986-9_21},
  month     = oct,
  abstract  = {Sowohl f{\"{u}}r die Installation, Inbetriebnahme und Diagnose eines Funknetzwerks,
               als auch zur Ortung von mobilen Kommunikationsendger{\"{a}}ten, ist es von gro\ss{}er Bedeutung,
               die Ausbreitungseigenschaften des elektromagnetischen Feldes der Sendeantennen zu kennen.
               In dieser Arbeit wird ein Robotersystem zur automatischen Vermessung der {\"{o}}rtlichen
               Signalcharakteristik der Funksender am Beispiel von Feldst{\"{a}}rkemessungen vorgestellt.}
}

@inproceedings{ECC03_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{D}ata {V}alidation in the {P}resence of {I}mprecisely {K}nown {C}orrelations},
  booktitle = {Proceedings of the 2003 European Control Conference 2003 (ECC 2003)},
  year      = {2003},
  address   = {Cambridge, United Kingdom},
  month     = sep,
  abstract  = {This paper derives fundamental results for data validation in
               the presence of imprecisely known correlations. Given a constraint
               on the maximum absolute correlation of a given estimate
               and measurement data, a tight upper bound for the joint
               covariance matrix is derived, which finally yields a modified
               Mahalanobis distance. The special cases of one-dimensional
               and two-dimensional random variables are discussed.},
  pdf       = {ECC03_Hanebeck.pdf}
}

@inproceedings{ECC03_Horn-SetBasedLocalization,
  author    = {Joachim Horn and Uwe D. Hanebeck and Konrad Riegel and Kai Heesche and Werner Hauptmann},
  title     = {{N}onlinear {S}et-{T}heoretic {P}osition {E}stimation of {C}ellular {P}hones},
  booktitle = {Proceedings of the 2003 European Control Conference (ECC 2003)},
  year      = {2003},
  address   = {Cambridge, United Kingdom},
  month     = sep,
  abstract  = {Within the existing GSM standard, several measurements are
               available that can be used for estimating the position of a cellular
               phone. First, the timing advance (TA) gives an estimate
               for the distance to the serving base station. Second, the signal
               strengths (RXLEV) of neighbouring base stations can also be
               interpreted as distance information. Both TA and RXLEV are
               subject to measurement errors caused for example by shadowing,
               reflections, and fast fading. Thus, a nonlinear set-theoretic
               estimation technique based on pseudo ellipsoids is applied. The
               uncertainty regions in the original space defined by the measurements
               are transformed into a hyperspace of higher dimension
               and described by pseudo ellipsoids. An approximation of
               the set intersection of the pseudo ellipsoids can be calculated
               recursively by a linear set-theoretic filter. The resulting pseudo
               ellipsoid is transformed back into the original space, and the
               position estimate is calculated as center of gravity of the resulting
               uncertainty region. The algorithm is evaluated based on
               the data of an extensive field trial in a rural area. Compared
               to Cell ID, the accuracy is significantly increased by using TA
               and RXLEV, reducing the mean error by half.},
  pdf       = {ECC03_Horn-SetBasedLocalization.pdf}
}

@inproceedings{SYSID03_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{O}ptimal {F}iltering of {N}onlinear {S}ystems {B}ased on {P}seudo {G}aussian {D}ensities},
  booktitle = {Proceedings of the 13th IFAC Symposium on System Identification (SYSID 2003)},
  year      = {2003},
  pages     = {331--336},
  address   = {Rotterdam, Netherlands},
  month     = aug,
  abstract  = {We consider the problem of estimating the state of a discrete-time
               dynamic system comprising a linear system equation and a nonlinear measurement
               equation based on measurements corrupted by non-Gaussian noise. The problem is
               solved by recursively calculating the complete posterior density of the state given
               the measurements. For representing the resulting non-Gaussian posterior, a new
               exponential type density, the so called pseudo Gaussian density, is introduced. By
               converting the original nonlinear system to an equivalent linear representation in
               a higher-dimensional space, the parameters of the pseudo Gaussian posterior are
               obtained by means of a linear estimator operating in the higher-dimensional space.
               The resulting filtering algorithms are easy to implement and always guarantee valid
               posterior densities.},
  pdf       = {SYSID03_Hanebeck.pdf}
}

@inproceedings{MFI03_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{P}rogressive {B}ayesian {E}stimation for {N}onlinear {D}iscrete-{T}ime {S}ystems: {T}he {M}easurement {S}tep},
  booktitle = {Proceedings of the 2003 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2003)},
  year      = {2003},
  pages     = {173--178},
  address   = {Tokyo, Japan},
  month     = jul,
  pdf       = {MFI03_Hanebeck.pdf},
  abstract  = {This paper is concerned with estimating the internal state
               of a dynamic system by processing measurements taken from
               the system output. An exact analytic representation of the
               probability density functions characterizing the estimate may
               not be possible to obtain. Even when available, it may be
               too complex or not practical because, for example, recursive
               application is required. Hence, approximations are generally
               inevitable. Gaussian mixture approximations are convenient
               for a number of reasons. However, calculating appropriate
               mixture parameters that minimize a global measure of deviation
               from the true density is a tough optimization task. Here,
               we propose a new approximation method that minimizes the
               squared integral deviation between the the true density and
               its mixture approximation. Rather than trying to solve the
               original problem, it is converted into a corresponding system
               of explicit ordinary first-order differential equations. This
               system of differential equations is then solved over a finite
               "time" interval, which is an efficient way of calculating the
               desired optimal parameter values. For polynomial measurement
               nonlinearities, closed-form analytic expressions for the
               coefficients of the system of differential equations are derived.}
}

@inproceedings{SPIE03_HanebeckBriechle-ProgBayes,
  author    = {Uwe D. Hanebeck and Kai Briechle and Andreas Rauh},
  title     = {{P}rogressive {B}ayes: {A} {N}ew {F}ramework for {N}onlinear {S}tate {E}stimation},
  booktitle = {Proceedings of SPIE, AeroSense Symposium},
  year      = {2003},
  volume    = {5099},
  pages     = {256 -- 267},
  address   = {Orlando, Florida, USA},
  abstract  = {This paper is concerned with recursively estimating the internal state of a nonlinear dynamic system by processing
               noisy measurements and the known system input. In the case of continuous states, an exact analytic
               representation of the probability density characterizing the estimate is generally too complex for recursive estimation
               or even impossible to obtain. Hence, it is replaced by a convenient type of approximate density characterized
               by a finite set of parameters. Of course, parameters are desired that systematically minimize a given measure of
               deviation between the (often unknown) exact density and its approximation, which in general leads to a complicated
               optimization problem. Here, a new framework for state estimation based on progressive processing is
               proposed. Rather than trying to solve the original problem, it is exactly converted into a corresponding system
               of explicit ordinary first-order differential equations. Solving this system over a finite "time" interval yields the
               desired optimal density parameters.},
  month     = may,
  pdf       = {SPIE03_HanebeckBriechle-ProgBayes.pdf}
}

@inproceedings{SPIE03_Horn,
  author    = {Joachim Horn and Uwe D. Hanebeck and Konrad Riegel and Kai Heesche and Werner Hauptmann},
  title     = {{N}onlinear {S}et--{T}heoretic {P}osition {E}stimation of {C}ellular {P}hones},
  booktitle = {Proceedings of SPIE, Vol. 5084, AeroSense Symposium},
  year      = {2003},
  pages     = {51--58},
  address   = {Orlando, Florida, USA},
  month     = may,
  pdf       = {SPIE03_Horn.pdf}
}

@inproceedings{SPIE03_Nitzsche,
  author    = {Norbert Nitzsche and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}otion {C}ompression for {T}elepresent {W}alking in {L}arge-{S}cale {R}emote {E}nvironments},
  booktitle = {Proceedings of SPIE, Vol. 5079, AeroSense Symposium},
  year      = {2003},
  pages     = {265--276},
  address   = {Orlando, Florida, USA},
  month     = may,
  owner     = {klumpp},
  abstract  = {Telepresent walking creates the sensation of walking through a target environment, which is not directly accessible
               to a human, e.g. because it is remote, hazardous, or of inappropriate scale. A mobile teleoperator replicates user
               motion and collects visual and auditory information from the target environment, which is then sent and displayed
               to the user. While walking freely about the user environment, the user perceives the target environment with the
               sensors of the teleoperator and feels as if walking through the target environment. Without additional processing
               of the user's motion data, the size of the target environment to be explored is limited to the size of the user
               environment. Motion compression extends telepresent walking to arbitrarily large target environments without
               making use of scaling or walking-in-place metaphors. Both travel distances and turning angles are mapped with
               ratio 1:1.},
  pdf       = {SPIE03_Nitzsche.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{SPIE03_Rauh,
  author    = {Andreas Rauh and Kai Briechle and Uwe D. Hanebeck and Clemens Hoffmann and Joachim Bamberger and Marian Grigoras},
  title     = {{L}ocalization of {DECT} {M}obile {P}hones {B}ased on a {N}ew {N}onlinear {F}iltering {T}echnique},
  booktitle = {Proceedings of SPIE, Vol. 5084, AeroSense Symposium},
  year      = {2003},
  pages     = {39--50},
  address   = {Orlando, Florida, USA},
  month     = may,
  owner     = {klumpp},
  abstract  = {In this paper, nonlinear Bayesian filtering techniques are applied to the localization of mobile radio communication
               devices. The application of this approach is demonstrated for the localization of DECT mobile telephones in
               a scenario with several base stations and a mobile handset. The received signal power, measured by the mobile
               handsets, is related to their position by nonlinear measurement equations. These consist of a deterministic part,
               modeling the received signal power as a function of the position, and a stochastic part, describing model errors
               and measurement noise. Additionally, user models are considered, which express knowledge about the motion
               of the user of the handset. The new Prior Density Splitting Mixture Estimator (PDSME), a Gaussian mixture
               filtering algorithm, significantly improves the localization quality compared to standard filtering techniques as
               the Extended Kalman Filter (EKF).},
  pdf       = {SPIE03_Rauh.pdf},
  timestamp = {2007.05.23}
}

@article{SPL03_Rauh,
  author    = {Andreas Rauh and Uwe D. Hanebeck},
  title     = {{C}alculating {M}oments of {E}xponential {D}ensities {U}sing {D}ifferential {A}lgebraic {E}quations},
  journal   = {IEEE Signal Processing Letters},
  year      = {2003},
  volume    = {10},
  pages     = {144--147},
  number    = {5},
  month     = may,
  owner     = {klumpp},
  pdf       = {SPL03_Rauh.pdf},
  timestamp = {2007.05.23}
}

@article{AT03_Nitzsche,
  author    = {Norbert Nitzsche and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}obile haptische {S}chnittstellen f{\"u}r weitr{\"a}umige {T}elepr{\"a}senz: {I}dee und {M}ethodik},
  journal   = {at -- Automatisierungstechnik},
  year      = {2003},
  volume    = {51},
  pages     = {5--12},
  number    = {1},
  month     = jan,
  doi       = {10.1524/auto.51.1.5.18872},
  owner     = {klumpp},
  pdf       = {AT03_Nitzsche.pdf},
  abstract  = {Dieser Beitrag besch{\"a}ftigt sich mit Telepr{\"a}senzsystemen zur Interaktion mit weitr{\"a}umigen
               realen oder virtuellen Zielumgebungen. Die daf{\"u}r notwendige Fortbewegung wird durch
               freie, gehende Fortbewegung des Anwenders erreicht. Um gleichzeitig haptische Interaktion
               mit einer Zielumgebung zu erm{\"o}glichen, wird die haptische Schnittstelle mit dem Anwender
               mitgef{\"u}hrt. Neuartig ist dabei der Einsatz einer mobilen haptischen Schnittstelle, die der Fortbewegung
               des Anwenders aktiv folgt. Basierend auf den dargestellten Methoden wurde ein
               prototypisches Telepr{\"a}senzsystem zur haptischen Exploration von ausgedehnten virtuellen
               Umgebungen entwickelt und getestet.
               
               This article is concerned with telepresence systems for interaction with extended real or virtual
               target environments. Locomotion required for this type of interaction is governed by free,
               walking locomotion of the operator. In order to allow haptic interaction with the target environment
               simultaneously, a haptic interface following the operator is employed. As a novel
               approach, we present a mobile haptic interface, which actively follows the operator's locomotion.
               Based on the presented methodology, a prototypic telepresence system for haptic
               exploration of extended virtual environments has been implemented and tested.},
  publisher = {Oldenbourg Verlag},
  timestamp = {2007.05.23}
}

@article{DGON02_Horn,
  author    = {Joachim Horn and Uwe D. Hanebeck and Konrad Riegel and Kai Heesche and Werner Hauptmann},
  title     = {{N}onlinear {S}et--{T}heoretic {P}osition {E}stimation of {C}ellular {P}hones},
  journal   = {Ortung und Navigation, Deutsche Gesellschaft f{\"u}r Ortung und Navigation e.V. (DGON)},
  year      = {2002},
  volume    = {1},
  pages     = {93--99},
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{HCRS02_Nitzsche,
  author    = {Norbert Nitzsche and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{E}xtending {T}elepresent {W}alking by {M}otion {C}ompression},
  booktitle = {1. SFB-Aussprachetag Human Centered Robotic Systems (HCRS 2002)},
  year      = {2002},
  pages     = {83--90},
  address   = {Karlsruhe, Germany},
  month     = dec,
  owner     = {klumpp},
  abstract  = {Telepresent walking allows visiting remote places
               such as museums, exhibitions or industrial sites with a
               high degree of realism. While walking freely around in
               the user environment, the user sees the remote environment
               "through the eyes" of a remote mobile teleoperator.
               For that purpose, the user's motion is tracked
               and transferred to the teleoperator. Without additional
               processing of the motion data, the size of the remote
               environment to be explored is limited to the size of the
               user environment. This paper proposes an extension
               of telepresent walking to arbitrarily large remote or
               virtual spaces based on compressing wide area motion
               into the available user space. Motion Compression is
               a novel technique and does not make use of scaling or
               walking-in-place metaphors. Turning angles and travel
               distances are mapped with ratio 1:1.},
  pdf       = {HCRS02_Nitzsche.pdf},
  timestamp = {2007.05.23}
}

@phdthesis{Habil02_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{N}onlinear {M}ethods for {S}tate {E}stimation in {S}tochastic {D}ynamic {S}ystems --- {A} {C}oncise {I}ntroduction},
  school    = {Department of Electrical Engineering and Information Technology, Technische Universit{\"a}t M{\"u}nchen, Referent: G.~Schmidt, Korreferent: V. Krebs},
  year      = {2002},
  type      = {Habilitation Thesis},
  month     = sep,
  owner     = {klumpp},
  timestamp = {2007.05.24}
}

@inproceedings{DSP02_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Sascha A. Stoeter and Paul E. Rybski and Maria Gini and Nikolaos Papanikolopoulos},
  title     = {{V}isual {S}ervoing of a {M}iniature {R}obot {T}oward a {M}arked {T}arget},
  booktitle = {Proceedings of the IEEE International Conference on Digital Signal Processing},
  year      = {2002},
  address   = {Santorini, Greece},
  month     = jul,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ICRA02_Lorch,
  author    = {Oliver Lorch and Javier F. Seara and Klaus H. Strobl and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{P}erception {E}rrors in {V}ision {G}uided {W}alking: {A}nalysis, {M}odeling, and {F}iltering},
  booktitle = {Proceedings of the 2002 IEEE International Conference on Robotics and Automation (ICRA 2002)},
  year      = {2002},
  url       = {https://ieeexplore.ieee.org/document/1014842/},
  address   = {Washington D.C., USA},
  month     = may,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{LOCELLUS02_Horn,
  author    = {Joachim Horn and Uwe D. Hanebeck and Konrad Riegel and Kai Heesche and Werner Hauptmann},
  title     = {{N}onlinear {S}et--{T}heoretic {P}osition {E}stimation of {C}ellular {P}hones},
  booktitle = {International Symposium on Location Based Services for Cellular Users (LOCELLUS 2002)},
  year      = {2002},
  address   = {Munich, Germany},
  month     = apr,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ECC01_Hanebeck-SetTheoreticEst,
  author    = {Uwe D. Hanebeck},
  title     = {{A} {S}quare--{R}oot {A}lgorithm for {S}et {T}heoretic {S}tate {E}stimation},
  booktitle = {Proceedings of the 2001 European Control Conference (ECC 2001)},
  year      = {2001},
  address   = {Porto, Portugal},
  month     = sep,
  owner     = {klumpp},
  pdf       = {ECC01_Hanebeck-SetTheoreticEst.pdf},
  abstract  = {This paper presents a modified set theoretic framework
               for estimating the state of a linear dynamic system based
               on uncertain measurements. The measurement errors are
               assumed to be unknown but bounded by ellipsoidal sets.
               Based on this assumption, a recursive state estimator is
               (re-)derived in a tutorial fashion. It comprises both the
               prediction step (time update), i.e., propagation of a set
               of feasible states by means of the system model and the
               filter step (measurement update), i.e., inclusion of a new
               measurement into the current estimate. The main contribution
               is an efficient square-root formulation of this
               estimator, which is well suited especially for practical
               applications.},
  timestamp = {2007.05.23}
}

@inproceedings{ECC01_HanebeckHorn-MixStochSetUncertainty,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{N}ew {E}stimators {f}or {M}ixed {S}tochastic and {S}et {T}heoretic {U}ncertainty {M}odels: {T}he {G}eneral {C}ase},
  booktitle = {Proceedings of the 2001 European Control Conference (ECC 2001)},
  year      = {2001},
  address   = {Porto, Portugal},
  month     = sep,
  owner     = {klumpp},
  pdf       = {ECC01_HanebeckHorn-MixStochSetUncertainty.pdf},
  abstract  = {New filters are derived for estimating the n-dimensional
               state of a linear dynamic system based on uncertain m-
               dimensional observations, which suffer from two types of
               uncertainties simultaneously. The first uncertainty is a
               stochastic process with given distribution. The second uncertainty
               is only known to be bounded, the exact underlying
               distribution is unknown. The new estimators combine
               set theoretic and stochastic estimation in a rigorous manner
               and provide a continuous transition between the two
               classical estimation concepts. They converge to a set theoretic
               estimator, when the stochastic error goes to zero,
               and to a Kalman filter, when the bounded error vanishes.
               In the mixed noise case, solution sets are provided that
               are uncertain in a stochastic sense.},
  timestamp = {2007.05.23}
}

@inproceedings{ROMAN01_Nitzsche,
  author    = {Norbert Nitzsche and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}obile {H}aptic {I}nteraction with {E}xtended {R}eal or {V}irtual {E}nvironments},
  booktitle = {Proceedings of the 10th IEEE International Workshop on Robot--Human Interactive Communication (ROMAN 2001)},
  year      = {2001},
  address   = {Bordeaux/Paris, France},
  month     = sep,
  abstract  = {A mobile interface for physical interaction with extended real or
               virtual environments is presented, which frees the operator from being bound to
               a stationary workstation. The operator's unrestricted walking is available as input
               to the locomotion control of the teleoperator or the avatar. Hence, the operator
               uses proprioceptive perception of locomotion, which is required for navigation and
               wayfinding. To allow unrestricted local locomotion, the haptic interface moves
               along with the operator. This could be achieved by using a portable kinesthetic
               interface, which, however, is not capable of displaying large external forces.
               A mobile haptic interface, which actively follows the operator's locomotion.
               The corresponding locomotion platform is continuously positioned in such a way
               that maximum manipulability is guaranteed. In order to accomplish this under
               the kinematic and dynamic restrictions of the wheel-based platform, the operator's
               intention of locomotion is predicted. A prototype telepresence system for
               kinesthetic exploration of extended virtual environments has been designed
               according to the proposed paradigm, implemented, and tested.},
  pdf       = {ROMAN01_Nitzsche.pdf}
}

@inproceedings{MFI01_Hanebeck-PseudoEllipsoids,
  author    = {Uwe D. Hanebeck},
  title     = {{R}ecursive {N}onlinear {S}et--{T}heoretic {E}stimation {B}ased on {P}seudo--{E}llipsoids},
  booktitle = {Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)},
  year      = {2001},
  pages     = {159--164},
  address   = {Baden--Baden, Germany},
  month     = aug,
  owner     = {klumpp},
  pdf       = {MFI01_Hanebeck-PseudoEllipsoids.pdf},
  abstracts = {In this paper, the problem of estimating a vector
               x of unknown quantities based on a set of measurements
               depending nonlinearly on x is considered. The
               measurements are assumed to be taken sequentially
               and are corrupted by unknown but bounded uncertainties.
               For this uncertainty model, a systematic design
               approach is introduced, which yields closed-form
               expressions for the desired nonlinear estimates. The
               estimates are recursively calculated and provide solution
               sets X containing the feasible sets, i.e., the sets
               of all x consistent with all the measurements available
               and their associated bounds. The sets X are tight
               upper bounds for the exact feasible sets and are in
               general not convex and not connected. The proposed
               design approach is versatile and the resulting nonlinear
               filter algorithms are both easy to implement and
               efficient.},
  timestamp = {2007.05.23}
}

@inproceedings{MFI01_HanebeckBriechle-UnknownCorr,
  author    = {Uwe D. Hanebeck and Kai Briechle},
  title     = {{N}ew {R}esults for {S}tochastic {P}rediction and {F}iltering {w}ith {U}nknown {C}orrelations},
  booktitle = {Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)},
  year      = {2001},
  pages     = {147--152},
  address   = {Baden--Baden, Germany},
  month     = aug,
  owner     = {klumpp},
  pdf       = {MFI01_HanebeckBriechle-UnknownCorr.pdf},
  abstract  = {This paper considers state estimation for dynamic
               systems in the case of nonwhite, mutually correlated
               noise processes. Here, the problem is complicated
               by the fact, that only the individual covariances are
               known; cross covariances between random variables
               obtained by taking individual noise processes at different
               time steps and between different noise processes
               are completely unknown. New estimator equations
               for solving this problem are derived in feedback form
               for both the prediction step and for the filtering step
               based on existing ideas known as covariance intersection.
               Solutions are given for the most general case
               of updating an N-dimensional state vector estimate
               based on M-dimensional observations. Furthermore,
               computationally efficient solutions for obtaining minimum
               covariance estimates are derived to avoid numerical
               optimization otherwise required.},
  timestamp = {2007.05.23}
}

@inproceedings{MFI01_HanebeckHorn-CovarianceBound,
  author    = {Uwe D. Hanebeck and Kai Briechle and Joachim Horn},
  title     = {{A} {T}ight {B}ound for the {J}oint {C}ovariance of {T}wo {R}andom {V}ectors with {U}nknown but {C}onstrained {C}ross--{C}orrelation},
  booktitle = {Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)},
  year      = {2001},
  pages     = {85--90},
  address   = {Baden--Baden, Germany},
  month     = aug,
  owner     = {klumpp},
  pdf       = {MFI01_HanebeckHorn-CovarianceBound.pdf},
  abstract  = {This paper derives a fundamental result for processing
               two correlated random vectors with unknown
               cross-correlation, where constraints on the maximum
               absolute correlation coefficient are given. A tight upper
               bound for the joint covariance matrix is derived
               on the basis of the individual covariances and the correlation
               constraint. For symmetric constraints, the
               bounding covariance matrix naturally possesses zero
               cross covariances, which further increases their usefulness
               in applications. Performance is demonstrated
               by recursively propagating a state through a linear
               dynamical system suffering from stochastic noise correlated
               with the system state.},
  timestamp = {2007.05.23}
}

@inproceedings{ACC01_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Optimal Filtering for Polynomial Measurement Nonlinearities with Additive Non-Gaussian Noise}},
  booktitle = {Proceedings of the 2001 American Control Conference (ACC 2001)},
  year      = {2001},
  address   = {Arlington, Virginia, USA},
  month     = jun,
  abstract  = {We consider the problem of estimating the n-dimensional state of a
               dynamic system based on m-dimensional discrete-time measurements.
               The measurements depend nonlinearly on the state and are corrupted
               by white non-Gaussian noise. The problem is solved by recursively
               calculating the complete posterior density of the state given the
               measurements. For that purpose, a new exponential type density is
               introduced, the so called pseudo Gaussian density, which is used
               to represent the complicated non-Gaussian posterior densities resulting
               from the recursion. For polynomial measurement nonlinearities and
               pseudo Gaussian noise densities, it is shown that the result of the
               optimal Bayesian measurement update is exactly obtained by a Kalman
               filter operating in a higher dimensional space. The resulting filtering
               algorithms are easy to implement and always guarantee valid posterior
               densities},
  pages     = {5028--5033},
  pdf       = {ACC01_Hanebeck.pdf}
}

@inproceedings{ACC01_HanebeckHorn-MixStochSetUncertainty,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{N}ew {E}stimators for {M}ixed {S}tochastic and {S}et {T}heoretic {U}ncertainty {M}odels: {T}he {G}eneral {C}ase},
  booktitle = {Proceedings of the 2001 American Control Conference (ACC 2001)},
  year      = {2001},
  address   = {Arlington, Virginia, USA},
  month     = jun,
  abstract  = {New filters are derived for estimating the n-dimensional state of
               a linear dynamic system based on uncertain m-dimensional observations,
               which suffer from two types of uncertainties simultaneously. The
               first uncertainty is a stochastic process with given distribution.
               The second uncertainty is only known to be bounded, the exact underlying
               distribution is unknown. The new estimators combine set theoretic
               and stochastic estimation in a rigorous manner and provide a continuous
               transition between the two classical estimation concepts. They converge
               to a set theoretic estimator, when the stochastic error goes to zero,
               and to a Kalman filter, when the bounded error vanishes. In the mixed
               noise case, solution sets are provided that are uncertain in a stochastic
               sense},
  pages     = {5040--5045}
}

@inproceedings{GMA01_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{N}eue {E}rgebnisse zur {Z}ustandssch{\"a}tzung bei simultan auftretenden mengenbasierten und stochastischen {U}nsicherheiten},
  booktitle = {Tagungsband des GMA--Kongress Automatisierungstechnik im Spannungsfeld neuer Technologien 2001},
  year      = {2001},
  address   = {Baden--Baden, Germany},
  month     = may,
  owner     = {klumpp},
  abstract  = {Behandelt wird der allgemeine Fall der Schätzung des $n$--dimensionalen Zustands eines linearen dynamischen Systems auf der Grundlage
               von unsicheren $m$--dimensionalen Messungen, die simultan unter zwei unterschiedlichen Formen von Unsicherheit leiden. Die erste Unsicherheit ist
               durch einen stochastischen Prozess mit bekannter Charakteristik gegeben. Die zweite wird nur durch ihre Wertbegrenzung beschrieben, die
               Verteilungsdichte ist jedoch unbekannt. Das neue Filter verallgemeinert bekannte Konzepte wie das Kalman--Filter und das mengenbasierte Filter,
               und umfasst diese als Grenzfälle.},
  pdf       = {GMA01_HanebeckHorn.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{SPIE01_BriechleHanebeck_CrossCorr,
  author    = {Kai Briechle and Uwe D. Hanebeck},
  title     = {{T}emplate {M}atching {u}sing {F}ast {N}ormalized {C}ross {C}orrelation},
  booktitle = {Proceedings of SPIE, Vol. 4387, AeroSense Symposium},
  year      = {2001},
  address   = {Orlando, Florida, USA},
  month     = apr,
  owner     = {klumpp},
  abstract  = {In this paper we present an algorithm for fast calculation
               of the normalized cross correlation (NCC) and its application to the problem
               of template matching. Given a template t, whose position is to be determined
               in an image f, the basic idea of the algorithm is to represent the template,
               for which the normalized cross correlation is calculated, as a sum of rectangular
               basis functions. Then the correlation is calculated for each basis function
               instead of the whole template. The result of the correlation of the template t
               and the image f is obtained as the weighted sum of the correlation functions
               of the basis functions. Depending on the approximation, the algorithm can by
               far outperform Fourier-transform based implementations of the normalized cross
               correlation algorithm and it is especially suited to problems, where many
               different templates are to be found in the same image f.},
  pdf       = {SPIE01_BriechleHanebeck_CrossCorr.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{SPIE01_HanebeckHorn-SLAM,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{A}n {E}fficient {M}ethod {f}or {S}imultaneous {M}ap {B}uilding {a}nd {L}ocalization},
  booktitle = {Proceedings of SPIE, Vol. 4385, AeroSense Symposium},
  year      = {2001},
  address   = {Orlando, Florida, USA},
  month     = apr,
  owner     = {klumpp},
  pdf       = {SPIE01_HanebeckHorn-SLAM.pdf},
  abstract  = {We consider the problem of simultaneously locating an observer and a set of environmental landmarks with respect
               to an inertial coordinate system, when both the observer position and the landmark positions are initially uncertain.
               For solving this problem, a new state estimator is introduced, which allows the problem to be consistently
               solved locally.},
  timestamp = {2007.05.23}
}

@article{AT00_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{Z}ustandssch{\"a}tzung im {F}all simultan auftretender mengenbasierter und stochastischer {U}nsicherheiten},
  journal   = {at -- Automatisierungstechnik},
  year      = {2000},
  volume    = {48},
  pages     = {265--272},
  number    = {6},
  url       = {https://www.degruyter.com/view/j/auto.2000.48.issue-6_2000/auto.2000.48.6.265/auto.2000.48.6.265.xml},
  owner     = {klumpp},
  publisher = {Oldenbourg Verlag},
  timestamp = {2007.05.23}
}

@article{IF00_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{F}using {I}nformation {S}imultaneously {C}orrupted {b}y {U}ncertainties {w}ith {K}nown {B}ounds {a}nd {R}andom {N}oise {w}ith {K}nown {D}istribution},
  journal   = {Information Fusion, Elsevier Science},
  year      = {2000},
  volume    = {1},
  pages     = {55--63},
  number    = {1},
  owner     = {klumpp},
  pdf       = {IF00_Hanebeck.pdf},
  timestamp = {2007.05.23}
}

@incollection{AMS00_Briechle,
  author    = {Kai Briechle and Uwe D. Hanebeck},
  title     = {{E}in {Z}wei--{K}arten--{V}erfahren zur gleichzeitigen {K}artographierung und {L}okalisierung},
  booktitle = {Autonome Mobile Systeme 2000 (AMS 2000), 16.~Fachgespr{\"a}ch, Karlsruhe, Informatik Aktuell},
  publisher = {Springer},
  year      = {2000},
  editor    = {R{\"u}diger Dillmann and Heinz W{\"o}rn and Markus von Ehr},
  month     = nov,
  url       = {https://link.springer.com/chapter/10.1007/978-3-642-59576-9_20},
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{RAAD00_Saldic,
  author    = {Nihad {\v{S}}aldi{\'{c}} and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}odular {W}heel {S}ystems for {O}mnidirectional {S}ervice {R}obots},
  booktitle = {Proceedings of the 9th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2000)},
  year      = {2000},
  pages     = {11--16},
  address   = {Maribor, Slovenia},
  month     = jun,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@incollection{Robotik00_Hanebeck,
  author    = {Uwe D. Hanebeck and Nihad \v{S}aldi\'{c} and Franz Freyberger and G{\"u}nther Schmidt},
  title     = {{M}odulare {R}adsatzsysteme f{\"u}r omnidirektionale mobile {R}oboter},
  booktitle = {Robotik 2000 Tagung (VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik), VDI Berichte 1552},
  publisher = {VDI, Berlin},
  year      = {2000},
  url       = {https://www.tib.eu/de/suchen/id/BLCP\%3ACN035163165/Modulare-Radsatzsysteme-fur-omnidirektionale-mobile/},
  pages     = {39--44},
  month     = jun,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@proceedings{AMS99_Schmidt,
  title     = {{A}utonome {M}obile {S}ysteme 1999, 15.~{F}achgespr{\"a}ch, {M}{\"u}nchen, 26.-27. {N}ovember 1999},
  year      = {1999},
  editor    = {G{\"u}nther Schmidt and Uwe D. Hanebeck and Franz Freyberger},
  series    = {Informatik Aktuell},
  publisher = {Springer},
  booktitle = {AMS},
  isbn      = {3-540-66732-6}
}

@inproceedings{CDC99_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{N}ew {E}stimators {f}or {M}ixed {S}tochastic {a}nd {S}et {T}heoretic {U}ncertainty {M}odels: {T}he {S}calar {M}easurement {C}ase},
  booktitle = {Proceedings of the 1999 IEEE Conference on Decision and Control (CDC 1999)},
  year      = {1999},
  pages     = {1934--1939},
  address   = {Phoenix, Arizona, USA},
  month     = dec,
  owner     = {klumpp},
  pdf       = {CDC99.pdf},
  timestamp = {2007.05.23}
}

@incollection{AMS99_Briechle,
  author    = {Kai Briechle and Uwe D. Hanebeck},
  title     = {{S}imultane {L}okalisierung und {K}artenaufbau f{\"u}r einen mobilen {S}erviceroboter},
  booktitle = {Autonome Mobile Systeme 1999 (AMS 1999), 15.~Fachgespr{\"a}ch, M{\"u}nchen, Informatik Aktuell},
  publisher = {Springer},
  year      = {1999},
  editor    = {G{\"u}nther Schmidt and Uwe D. Hanebeck and Franz Freyberger},
  pages     = {200--210},
  month     = nov,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{IROS99_HanebeckHorn-StochSetUncert,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{A} {N}ew {C}oncept {f}or {S}tate {E}stimation {i}n {t}he {P}resence {o}f {S}tochastic and {S}et {T}heoretic {U}ncertainties},
  booktitle = {Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999)},
  year      = {1999},
  address   = {Kjongju, Republic of Korea},
  month     = oct,
  owner     = {klumpp},
  pdf       = {IROS99_SSI.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{IROS99_HanebeckSaldic-WheelSystem,
  author    = {Uwe D. Hanebeck and Nihad \v{S}aldi\'{c}},
  title     = {{A} {M}odular {W}heel {S}ystem for {M}obile {R}obot {A}pplications},
  booktitle = {Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999)},
  year      = {1999},
  address   = {Kjongju, Republic of Korea},
  month     = oct,
  owner     = {klumpp},
  pdf       = {IROS99_WHEEL.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{SMC99_Briechle-SLAM,
  author    = {Kai Briechle and Uwe D. Hanebeck},
  title     = {{S}elf--{L}ocalization {o}f {a} {M}obile {R}obot {U}sing {a} {F}ast {C}ross--{C}orrelation {A}lgorithm},
  booktitle = {Proceedings of the 1999 IEEE Systems, Man, and Cybernetics Conference (SMC 1999)},
  year      = {1999},
  address   = {Tokyo, Japan},
  month     = oct,
  owner     = {klumpp},
  pdf       = {SMC99_FNCC.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{SMC99_Hanebeck-MixStochSetUncert,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{N}ew {R}esults for {S}tate {E}stimation in the {P}resence of {M}ixed {S}tochastic and {S}et {T}heoretic {U}ncertainties},
  booktitle = {Proceedings of the 1999 IEEE Systems, Man, and Cybernetics Conference (SMC 1999)},
  year      = {1999},
  address   = {Tokyo, Japan},
  month     = oct,
  owner     = {klumpp},
  pdf       = {SMC99_SSI.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{ECC99_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{N}ew {E}stimators for {M}ixed {S}tochastic and {S}et {T}heoretic {U}ncertainty {M}odels: {T}he {V}ector {C}ase},
  booktitle = {Proceedings of the 5th European Control Conference (ECC 1999)},
  year      = {1999},
  address   = {Karlsruhe, Germany},
  month     = sep,
  owner     = {klumpp},
  pdf       = {ECC99.pdf},
  timestamp = {2007.05.23}
}

@article{AUTO99_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn and G{\"u}nther Schmidt},
  title     = {{O}n {C}ombining {S}tatistical {a}nd {S}et {T}heoretic {E}stimation},
  journal   = {Automatica},
  year      = {1999},
  volume    = {35},
  pages     = {1101--1109},
  number    = {6},
  month     = jun,
  owner     = {klumpp},
  pdf       = {AUTO99_Hanebeck.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{ICRA99_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{A} {N}ew {E}stimator {f}or {M}ixed {S}tochastic {a}nd {S}et {T}heoretic {U}ncertainty {M}odels {A}pplied {t}o {M}obile {R}obot {L}ocalization},
  booktitle = {Proceedings of the 1999 IEEE International Conference on Robotics and Automation (ICRA 1999)},
  year      = {1999},
  pages     = {1335--1340},
  address   = {Detroit, Michigan, USA},
  month     = may,
  owner     = {klumpp},
  pdf       = {ICRA99_SSI.pdf},
  timestamp = {2007.05.23}
}

@inproceedings{SPIE99_Hanebeck,
  author    = {Uwe D. Hanebeck and Joachim Horn},
  title     = {{A} {N}ew {S}tate {E}stimator {f}or {a} {M}ixed {S}tochastic {a}nd {S}et {T}heoretic {U}ncertainty {M}odel},
  booktitle = {Proceedings of SPIE, Vol. 3720, AeroSense Symposium},
  year      = {1999},
  pages     = {336--344},
  address   = {Orlando, Florida, USA},
  month     = apr,
  owner     = {klumpp},
  pdf       = {SPIE99.pdf},
  timestamp = {2007.05.23}
}

@incollection{ISER98_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}obile {R}obot {L}ocalization {B}ased on {E}fficient {P}rocessing of {S}ensor {D}ata and {S}et-theoretic {S}tate {E}stimation},
  booktitle = {Experimental Robotics V, The Fifth International Symposium, Barcelona, Catalonia, Lecture Notes in Control and Information Sciences 223},
  publisher = {Springer},
  year      = {1998},
  url       = {https://link.springer.com/chapter/10.1007/BFb0112978},
  editor    = {A. Casals and A. T. de Almeida},
  pages     = {385--396},
  address   = {Barcelona, Spain},
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@incollection{AMS98_Furtwaengler,
  author    = {Ralf Furtw{\"a}ngler and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{D}ynamische {B}{\"a}nder zur {B}ewegungsplanung f{\"u}r mobile {M}anipulatoren},
  booktitle = {Autonome Mobile Systeme 1998 (AMS 1998), 14.~Fachgespr{\"a}ch, Karlsruhe, Informatik Aktuell},
  publisher = {Springer},
  year      = {1998},
  url       = {https://link.springer.com/chapter/10.1007/978-3-642-60043-2_20},
  editor    = {Heinz W{\"o}rn and R{\"u}diger Dillmann and Dominik Henrich},
  pages     = {164--171},
  month     = nov,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ROMAN98_Schmidt,
  author    = {G{\"u}nther Schmidt and Uwe D. Hanebeck},
  title     = {{A}spects of {H}uman--{R}obot--{C}ommunication for a {S}emi--autonomous {R}obotic {A}ssistant for the {H}ealth {C}are {E}nvironment},
  booktitle = {Proceedings of 7th IEEE International Workshop on Robot and Human Communication (ROMAN 1998)},
  year      = {1998},
  pages     = {187-194},
  address   = {Takamatsu, Japan},
  month     = sep,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@article{JIRSTA98_Ettelt,
  author    = {Eugen Ettelt and Ralf Furtw{\"a}ngler and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{D}esign {I}ssues of a {S}emi--{A}utonomous {R}obotic {A}ssistant for the {H}ealth {C}are {E}nvironment},
  journal   = {Journal of Intelligent and Robotic Systems, Kluwer Academic Publishers},
  year      = {1998},
  volume    = {22},
  pages     = {191--209},
  number    = {3--4},
  url       = {https://link.springer.com/article/10.1023/A:1008082024638},
  month     = jul,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{AMC98_Furtwaengler,
  author    = {Ralf Furtw{\"a}ngler and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{D}ynamic {C}ontrol of a {M}obile {M}anipulator},
  booktitle = {Proceedings of the 5th International Workshop on Advanced Motion Control (AMC 1998)},
  year      = {1998},
  pages     = {440-445},
  url       = {https://ieeexplore.ieee.org/document/743577/},
  address   = {Coimbra, Portugal},
  month     = jun,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ICRA98_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{P}ipelined {S}ampling {T}echniques for {S}onar {T}racking {S}ystems},
  booktitle = {Proceedings of the 1998 IEEE International Conference on Robotics and Automation (ICRA 1998)},
  year      = {1998},
  pages     = {2813-2818},
  url       = {https://ieeexplore.ieee.org/document/680477/},
  address   = {Leuven, Belgium},
  month     = may,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@phdthesis{Diss97_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{L}okalisierung eines mobilen {R}oboters mittels effizienter {A}uswertung von {S}ensordaten und mengenbasierter {Z}ustandssch{\"a}tzung},
  school    = {TU M{\"u}nchen},
  comment   = {Referent: G.~Schmidt, Korreferent: E.~D.~Dickmanns, Fortschrittsberichte VDI, Reihe 8: Me{\ss}-, Steuerungs- und Regelungstechnik, Nr. 643, VDI Verlag, D{\"u}sseldorf},
  year      = {1997},
  type      = {Dissertation},
  note      = {ISBN 3-18-364308-1},
  owner     = {klumpp},
  pdf       = {Diss_Hnb_1997.pdf},
  timestamp = {2007.05.24}
}

@inproceedings{IARP97_Hanebeck,
  author    = {G{\"u}nther Schmidt and Uwe D. Hanebeck and Christian Fischer},
  title     = {{A} {M}obile {S}ervice {R}obot for the {H}ospital and {H}ome {E}nvironment},
  booktitle = {International Advanced Robotics Programme (IARP 1997), Proceedings of the Second International Workshop on Service and Personal Robots: Technologies and Applications},
  year      = {1997},
  address   = {Genova, Italy},
  month     = oct,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{IROS97_Hanebeck,
  author    = {Uwe D. Hanebeck and Christian Fischer and G{\"u}nther Schmidt},
  title     = {{ROMAN}: {A} {M}obile {R}obotic {A}ssistant for {I}ndoor {S}ervice {A}pplications},
  booktitle = {Proceedings of the 1997 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS 1997)},
  year      = {1997},
  pages     = {518-525},
  address   = {Grenoble, France},
  month     = sep,
  url       = {https://ieeexplore.ieee.org/document/655061/},
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ISER97_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{M}obile {R}obot {L}ocalization {B}ased on {E}fficient {P}rocessing of {S}ensor {D}ata and {S}et--theoretic {S}tate {E}stimation},
  booktitle = {Fifth International Symposium on Experimental Robotics (ISER 1997)},
  year      = {1997},
  pages     = {321-332},
  address   = {Barcelona, Spain},
  month     = jun,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@article{CEP96_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{L}ocalization of {F}ast {M}obile {R}obots {B}ased on an {A}dvanced {A}ngle--{M}easurement {T}echnique},
  journal   = {IFAC Control Engineering Practice, Elsevier Science},
  year      = {1996},
  volume    = {4},
  pages     = {1109--1118},
  number    = {8},
  owner     = {klumpp},
  pdf       = {CEP96_Hanebeck.pdf},
  timestamp = {2007.05.23}
}

@article{FSS96_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{G}enetic {O}ptimization of {F}uzzy {N}etworks},
  journal   = {Journal of Fuzzy Sets and Systems, Special Issue on Neuro--Fuzzy
               Techniques and Applications, Elsevier Science},
  year      = {1996},
  volume    = {79},
  pages     = {59--68},
  owner     = {klumpp},
  pdf       = {FSS96_Hanebeck.pdf},
  timestamp = {2007.05.23}
}

@incollection{AMS96_Daxwanger,
  author    = {Wolfgang Daxwanger and Eugen Ettelt and Christian Fischer and Franz Freyberger and Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{ROMAN}: {E}in mobiler {S}erviceroboter als pers{\"o}nlicher {A}ssistent in belebten {I}nnenr{\"a}umen},
  booktitle = {Autonome Mobile Systeme 1996 (AMS 1996), 12.~Fachgespr{\"a}ch, M{\"u}nchen, Informatik aktuell},
  publisher = {Springer},
  url       = {https://link.springer.com/chapter/10.1007/978-3-642-80324-6_29},
  year      = {1996},
  editor    = {G{\"u}nther Schmidt and Franz Freyberger},
  pages     = {314--333},
  month     = oct,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ICASSP96_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{C}losed--{F}orm {E}lliptic {L}ocation with an {A}rbitrary {A}rray {T}opology},
  booktitle = {Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1996)},
  year      = {1996},
  url       = {https://ieeexplore.ieee.org/document/550524/},
  pages     = {3070-3073},
  address   = {Atlanta, Georgia, USA},
  month     = may,
  owner     = {klumpp},
  timestamp = {2007.05.23},
  pdf       = {ICASSP96_Hanebeck.pdf}
}

@inproceedings{ICRA96_Hanebeck-SetBayes,
  author    = {Uwe D. Hanebeck and Joachim Horn and G{\"u}nther Schmidt},
  title     = {{O}n {C}ombining {S}et {T}heoretic and {B}ayesian {E}stimation},
  booktitle = {Proceedings of the 1996 IEEE International Conference on Robotics and Automation (ICRA 1996)},
  year      = {1996},
  pages     = {3081-3086},
  address   = {Minneapolis, Minnesota, USA},
  month     = apr,
  url       = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=509180},
  owner     = {klumpp},
  timestamp = {2007.05.23},
  pdf       = {ICRA96_Hanebeck-SetBayes.pdf}
}

@inproceedings{ICRA96_HanebeckSchmidt-Angle,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{S}et theoretic {L}ocalization of {F}ast {M}obile {R}obots {U}sing an {A}ngle {M}easurement {T}echnique},
  booktitle = {Proceedings of the 1996 IEEE International Conference on Robotics and Automation (ICRA 1996)},
  year      = {1996},
  pages     = {1387-1394},
  address   = {Minneapolis, Minnesota, USA},
  month     = apr,
  owner     = {klumpp},
  pdf       = {ICRA96_HanebeckSchmidt-Angle.pdf},
  timestamp = {2007.05.23}
}

@incollection{IRS95_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{A} {N}ew {H}igh {P}erformance {M}ultisonar {S}ystem for {F}ast {M}obile {R}obot {A}pplications},
  booktitle = {Intelligent Robots and Systems},
  url       = {https://www.sciencedirect.com/science/article/pii/B9780444822505500013},
  publisher = {Elsevier Science},
  year      = {1995},
  editor    = {V. Graefe},
  pages     = {1--14},
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@incollection{AMS95_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{S}chnelle {O}bjektdetektion mit {U}ltraschallsensor--{A}rrays},
  booktitle = {Autonome Mobile Systeme 1995 (AMS 1995), 11.~Fachgespr{\"a}ch, Karlsruhe, Informatik Aktuell},
  publisher = {Springer},
  url       = {https://link.springer.com/chapter/10.1007/978-3-642-80064-1_17},
  year      = {1995},
  editor    = {R{\"u}diger Dillmann and Ulrich Rembold and Tim L{\"u}th},
  pages     = {162--171},
  month     = nov,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ICASAV95_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{A}bsolute {L}ocalization of {F}ast {M}obile {R}obots {B}ased on an {A}ngle {M}easurement {T}echnique},
  booktitle = {IFAC Workshop on Intelligent Components for Autonomous and Semi-Autonomous Vehicles (ICASAV 1995)},
  year      = {1995},
  pages     = {87-92},
  address   = {Toulouse, France},
  month     = oct,
  owner     = {klumpp},
  timestamp = {2007.05.23},
  pdf       = {ICASAV95_Hanebeck.pdf}
}

@incollection{KI95_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther K. Schmidt},
  title     = {{A}bsolute {L}ocalization of {F}ast {M}obile {R}obots {B}ased on an {A}ngle {M}easurement {T}echnique},
  booktitle = {19th Annual German Conference on Artificial Intelligence (KI'95), Bielefeld, KI--95 Activities: Workshops, Posters, Demos},
  publisher = {Gesellschaft f{\"u}r Informatik e.V.},
  year      = {1995},
  editor    = {Leonie Dreschler--Fischer and Simone Pribbenow},
  pages     = {164--166},
  address   = {Bielefeld, Germany},
  month     = sep,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{TELEMAN95_Maier,
  author    = {Stefan Maier and Uwe D. Hanebeck},
  title     = {{F}uzzy {G}uidance of {O}mnidirectional {M}obile {R}obots {I}ncluding {S}ensor--{B}ased {O}bstacle {A}voidance},
  booktitle = {Proceedings of the TELEMAN Student Research Projects Congress},
  year      = {1995},
  pages     = {95--100},
  address   = {Noordwijkerhout, Netherlands},
  month     = jul,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{ICONIP94_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther K. Schmidt},
  title     = {{O}ptimization of {F}uzzy {N}etworks via {G}enetic {A}lgorithms},
  booktitle = {Proceedings of the 1994 International Conference on Neural Information Processing (ICONIP 1994)},
  year      = {1994},
  pages     = {1583--1588},
  address   = {Seoul, Republic of Korea},
  month     = oct,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{EUFIT94_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{O}ptimization of {F}uzzy {N}etworks via {G}enetic {A}lgorithms},
  booktitle = {Proceedings of the 2nd European Congress on Intelligent Techniques and Soft Computing (EUFIT 1994)},
  year      = {1994},
  pages     = {1011--1013},
  address   = {Aachen, Germany},
  month     = sep,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@inproceedings{IROS94_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{A} {N}ew {H}igh {P}erformance {M}ultisonar {S}ystem for {F}ast {M}obile {R}obots},
  booktitle = {Proceedings of the 1994 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS 1994)},
  year      = {1994},
  pages     = {1853--1860},
  address   = {Munich, Germany},
  month     = sep,
  url       = {https://ieeexplore.ieee.org/document/407607/},
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@article{AT93_Hanebeck,
  author    = {Uwe D. Hanebeck and G{\"u}nther Schmidt},
  title     = {{E}xperimentelle {V}ertiefung der {A}usbildung im {F}uzzy-{L}ogik-{R}eglerentwurf},
  journal   = {at -- Automatisierungstechnik},
  year      = {1993},
  volume    = {41},
  pages     = {300--306},
  url       = {https://www.degruyter.com/view/j/auto.1993.41.issue-8/auto-1993-0806/auto-1993-0806.xml},
  number    = {8},
  owner     = {klumpp},
  publisher = {Oldenbourg Verlag},
  timestamp = {2007.05.23}
}

@inproceedings{IFAC93_Hanebeck,
  author    = {G{\"u}nther Schmidt and Uwe D. Hanebeck},
  title     = {{T}eaching {F}uzzy {L}ogic {C}ontrol {D}esign {T}hrough {L}aboratory {E}xperiments and {S}tudent {P}rojects},
  booktitle = {Proceedings of the 12th World Congress of the International Federation of Automatic Control (IFAC 1993)},
  year      = {1993},
  pages     = {147--150},
  address   = {Sydney, Australia},
  url       = {https://www.sciencedirect.com/science/article/pii/S1474667017483716},
  month     = jul,
  owner     = {klumpp},
  timestamp = {2007.05.23}
}

@mastersthesis{DA91_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{U}ntersuchungen zur {V}erwendung von {M}arkov-{P}rozessen f{\"u}r die {E}rzeugung von {S}prachmodellsignalen},
  school    = {Arbeitsgruppe Digitale Signalverarbeitung, Prof.~Dr.--Ing.~Ulrich Heute, Lehrstuhl f{\"u}r Nachrichtentechnik, Ruhr--Universit{\"a}t Bochum},
  year      = {1991},
  type      = {Diplomarbeit},
  owner     = {klumpp},
  timestamp = {2007.05.24}
}

@mastersthesis{SA89_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{A}ufbau einer {S}ignalprozessorkarte f{\"u}r den {DSP} 5011},
  school    = {{L}ehrstuhl f{\"u}r {N}achrichtentechnik, Prof.~Dr.~Dr.~h.c.~mult.~Alfred Fettweis, Ruhr--Universit{\"a}t Bochum},
  year      = {1989},
  type      = {Studienarbeit},
  owner     = {klumpp},
  timestamp = {2007.05.24}
}


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