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Particle filter with one-step randomly delayed measurements and unknown latency probability

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Listed:
  • Yonggang Zhang
  • Yulong Huang
  • Ning Li
  • Lin Zhao

Abstract

In this paper, a new particle filter is proposed to solve the nonlinear and non-Gaussian filtering problem when measurements are randomly delayed by one sampling time and the latency probability of the delay is unknown. In the proposed method, particles and their weights are updated in Bayesian filtering framework by considering the randomly delayed measurement model, and the latency probability is identified by maximum likelihood criterion. The superior performance of the proposed particle filter as compared with existing methods and the effectiveness of the proposed identification method of latency probability are both illustrated in two numerical examples concerning univariate non-stationary growth model and bearing only tracking.

Suggested Citation

  • Yonggang Zhang & Yulong Huang & Ning Li & Lin Zhao, 2016. "Particle filter with one-step randomly delayed measurements and unknown latency probability," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(1), pages 209-221, January.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:1:p:209-221
    DOI: 10.1080/00207721.2015.1056272
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    References listed on IDEAS

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    1. Wang, Xiaoxu & Liang, Yan & Pan, Quan & Zhao, Chunhui & Yang, Feng, 2014. "Design and implementation of Gaussian filter for nonlinear system with randomly delayed measurements and correlated noises," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 1011-1024.
    2. Shuli Sun & Wendong Xiao, 2013. "Optimal linear estimators for systems with multiple random measurement delays and packet dropouts," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(2), pages 358-370.
    3. Hua Han & Yongsheng Ding & Kuangrong Hao & Liangjian Hu, 2013. "Particle filter for state estimation of jump Markov nonlinear system with application to multi-targets tracking," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1333-1343.
    4. R. Caballero-Águila & A. Hermoso-Carazo & J. Linares-Pérez, 2013. "Linear estimation based on covariances for networked systems featuring sensor correlated random delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1233-1244.
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