Li, X., Liu, Y., Yang, L. et al. (2 more authors) (2025) On the variational Gaussian filtering with natural gradient descent. In: Proceedings of 2025 28th International Conference on Information Fusion (FUSION). 2025 28th International Conference on Information Fusion (FUSION), 07-11 Jul 2025, Rio de Janeiro, Brazil. Institute of Electrical and Electronics Engineers (IEEE). ISBN: 9798331503505.
Abstract
Variational Gaussian filter (VGF) approximates the intractable posterior of the state of a non-linear non-Gaussian system using a single Gaussian density normally found through Kullback-Leibler divergence minimization. This paper focuses on the VGFs whose measurement update is realized by employing the natural gradient descent (NGD). Under the assumption that the state predictive distribution is also Gaussian, we re-examine the iterative NGD-based measurement update under two different parameterizations of the Gaussian posterior. The first one consists of the mean and covariance, while the other comprises the mean and precision matrix (i.e., the inverse of the covariance). Their NGD-based update rules are derived in an alternative but unified way using matrix calculus. They are compared against each other and with the one developed using the natural parameterization of the Gaussian density. Important new insights are obtained. Modifications to the established update rules, which guarantee the positive definiteness of the covariance/precision matrix of the Gaussian posterior, are re-visited as well. Simulations are used to corroborate the theoretical results and evaluate the performance of the developed algorithms in range-bearing tracking.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). Except as otherwise noted, this author-accepted version of a paper published in Proceedings of 2025 28th International Conference on Information Fusion (FUSION) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Target tracking; Filtering; Estimation; Minimization; Calculus; Iterative methods; Covariance matrices; Convergence |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jun 2025 15:46 |
Last Modified: | 22 Sep 2025 09:31 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.23919/FUSION65864.2025.11124137 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227277 |