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Rao-Blackwellized Particle Filter for Asynchronously Dependent Noises

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  • Control Theory and Applications
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Abstract

This paper develops Rao-Blackwellized particle filter with asynchronous dependence between system noise and measurement noise. It is pointed out that this dependence affects both the particle filter update step for the nonlinear sub-system and the Kalman filter update step for the conditionally linear sub-system in Rao-Blackwellized particle filter. A de-correlation method is suggested to deal with such influence. The optimal importance density function for sampling the nonlinear sub-state is found out, and a suboptimal one for approximating the optimal importance density function is proposed. The proposed methods are applied to target tracking to testify their effectiveness and superiority.

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Authors and Affiliations

  1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, China

    Yunqi Chen

  2. School of Science, Harbin Institute of Technology-Shenzhen, Shenzhen, China

    Zhibin Yan

  3. School of Mathematics, Harbin Institute of Technology, Harbin, China

    **ng Zhang

Authors
  1. Yunqi Chen
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  2. Zhibin Yan
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  3. **ng Zhang
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Corresponding author

Correspondence to Zhibin Yan.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work is supported by Shenzhen Science and Technology Innovation Commission under Grant JCYJ20190806143607340.

Yunqi Chen received his Master’s degree in probability and mathematical statistics from Harbin Institute of Technology (HIT), in 2017. He is now a Ph.D. candidate in control science and engineering at HIT. His research interests include nonlinear filtering and smoothing, sequential Monte Carlo method and Bayesian parameter estimation.

Zhibin Yan received his Master’s and Ph.D. degrees in mathematics from Harbin Institute of Technology (HIT), in 1991 and 2002, respectively. He was a Professor at HIT from 2005 to 2017. Currently, he is a Professor in Harbin Institute of Technology-Shenzhen. His research interests include Markov process and stochastic analysis, convergence of parameter estimation, system identification and Monte Carlo method for nonlinear filtering.

**ng Zhang received her Master’s degree in operational research and cybernetics from Harbin Institute of Technology (HIT), in 2015. She is currently a Ph.D. candidate in Statistics at HIT. Her current research interests include nonlinear filtering, Monte Carlo method and Bayesian estimation.

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Chen, Y., Yan, Z. & Zhang, X. Rao-Blackwellized Particle Filter for Asynchronously Dependent Noises. Int. J. Control Autom. Syst. 19, 2026–2037 (2021). https://doi.org/10.1007/s12555-019-0832-8

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  • DOI: https://doi.org/10.1007/s12555-019-0832-8

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