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@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{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    = {Winner Best Paper Award Certificate (PDF)}
}

@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    = {Winner Best Paper Award: First Runner Up Certificate (PDF)}
}

@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    = {Winner Best Paper Award Certificate (PDF)}
}

@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    = {Best Poster Award Runner-Up}
}

@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    = {Winner Best Paper Award Certificate (PDF)}
}

@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

repository: KIT-ISAS/ISAS-Publications

repo_name: KIT-ISAS/ISAS-Publications