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A New MCMC Particle Filter Resampling Algorithm Based on Minimizing Sampling Variance

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Simulation Tools and Techniques (SIMUtools 2019)

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Abstract

In order to solve the problem of particle divergence caused by deviation of sample distribution before and after resampling, a new Markov Chain Monte Carlo (MCMC) resampling algorithm based on minimizing sampling variance is proposed. First, MCMC transfer in which Particle Swarm Optimization (PSO) is possessed as the transfer kernel to construct Markov Chain is applied to the impoverished sample to combat sample degeneracy as well as sample impoverishment. Second, the algorithm takes the weighted variance as the cost function to measure the difference between the weighted particle discrete distribution before and after the resampling process, and optimizes the previous MCMC resampling by the minimum sampling variance criterion. Finally Experiment result shows that the algorithm can overcome particle impoverishment and realize the identical distribution of particles before and after resampling.

This work is partly supported by the Natural Science Foundation of Jiangsu Province of China (No. BK20161165), the applied fundamental research Foundation of Xuzhou of China (No. KC17072), Xuzhou Science and Technology Plan Project (No. KC18011), and Ministry of Housing and Urban-Rural Development Science and Technology Planning Project (NO.2016-R2-060).

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Authors and Affiliations

  1. Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology, Xuzhou, 221018, China

    Juan Tian & Dan Li

Authors
  1. Juan Tian
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  2. Dan Li
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Correspondence to Juan Tian .

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Editors and Affiliations

  1. Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics Aeronautics, UESTC, Chengdu, China

    Dingde Jiang

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tian, J., Li, D. (2019). A New MCMC Particle Filter Resampling Algorithm Based on Minimizing Sampling Variance. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-32216-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32215-1

  • Online ISBN: 978-3-030-32216-8

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