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A variational marginalized particle filter for jump Markov nonlinear systems with unknown transition probabilities

Cheng, Cheng and Tourneret, Jean-Yves A variational marginalized particle filter for jump Markov nonlinear systems with unknown transition probabilities. (2021) Signal Processing, 188. 108226. ISSN 0165-1684

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Official URL: https://doi.org/10.1016/j.sigpro.2021.108226

Abstract

This paper studies a new variational marginalized particle filter for jointly estimating the state and the system mode parameters of jump Markov nonlinear systems. Contrary to the Markovian assumption usu- ally considered to model the evolution of the system modes, we introduce conjugate prior distributions for the system mode parameters. The joint posterior distribution of the state and system mode parame- ters is then marginalized with respect to the mode variables. The remaining state vector is sampled us- ing a sequential Monte Carlo algorithm, and the mode parameters are sampled using variational Bayesian inference. In order to obtain analytical solutions for the different variational distributions, we use an extended factorized approximation simplifying the variational distributions. A comprehensive simulation study is conducted to compare the performance of the proposed approach with the state-of-the-art for a modified nonlinear benchmark model and maneuvering target tracking scenarios.

Item Type:Article
HAL Id:hal-03382639
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Northwestern Polytechnical University (CHINA)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited On:19 Jul 2021 09:00

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