Li, X., Liu, Y., Yang, L. et al. (2 more authors) (Accepted: 2025) On the variational Gaussian filtering with natural gradient descend. In: Proceedings of the International Conference on Information Fusion FUSION 2025. 2025 28th International Conference on Information Fusion (FUSION), 07-11 Jul 2025, Rio de Janeiro, Brazil. Institute of Electrical and Electronics Engineers (IEEE) (In Press)
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 posterior Gaussian density. 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 a unified manner using matrix calculus. They are then 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 can guarantee the positive definiteness of the covariance/precision matrix of the Gaussian posterior, are re-visited as well. Simulation study is conducted to evaluate the performance of the developed algorithms in a range-bearing target tracking task.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). |
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: | 05 Jun 2025 15:46 |
Status: | In Press |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227277 |
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Filename: Fusion 2025_44_final.pdf
