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Article

Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation †

Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in K. Li, F. Pfaff, and U. D. Hanebeck, “Nonlinear von Mises–Fisher Filtering Based on Isotropic Deterministic Sampling”, in Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, 14–16 September 2020.
Academic Editor: Stefano Lenci
Sensors 2021, 21(9), 2991; https://doi.org/10.3390/s21092991
Received: 5 April 2021 / Revised: 19 April 2021 / Accepted: 21 April 2021 / Published: 24 April 2021
(This article belongs to the Special Issue Multisensor Fusion and Integration)
In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter. View Full-Text
Keywords: sensor fusion; recursive Bayesian estimation; directional statistics; unscented transform; nonlinear hyperspherical filtering sensor fusion; recursive Bayesian estimation; directional statistics; unscented transform; nonlinear hyperspherical filtering
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MDPI and ACS Style

Li, K.; Pfaff, F.; Hanebeck, U.D. Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation. Sensors 2021, 21, 2991. https://doi.org/10.3390/s21092991

AMA Style

Li K, Pfaff F, Hanebeck UD. Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation. Sensors. 2021; 21(9):2991. https://doi.org/10.3390/s21092991

Chicago/Turabian Style

Li, Kailai, Florian Pfaff, and Uwe D. Hanebeck. 2021. "Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation" Sensors 21, no. 9: 2991. https://doi.org/10.3390/s21092991

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