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A novel particle filter for extended target tracking with random hypersurface model

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Listed:
  • Zhang, Xing
  • Yan, Zhibin
  • Chen, Yunqi
  • Yuan, Yanhua

Abstract

In the random hypersurface model for extended target tracking problem, the scaling factor in the measurement equation brings difficulty for existing particle filter to calculate the likelihood in the weighting update stage. In this paper, we firstly simplify the existing approximate likelihood function where the distribution of the scaling factor is approximated by Gaussian one. Then, by directly dealing with the distribution of the scaling factor whose square has uniform distribution, we propose a novel explicit formula of the logarithm of likelihood. Based on this formula, a feasible weighting scheme is obtained and a novel particle filtering algorithm (NPFA) is proposed. Simulation shows that NPFA improves estimation accuracy compared with the existing unscented Kalman filter and particle filter for the tracking problem under discussion.

Suggested Citation

  • Zhang, Xing & Yan, Zhibin & Chen, Yunqi & Yuan, Yanhua, 2022. "A novel particle filter for extended target tracking with random hypersurface model," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    • Handle: RePEc:eee:apmaco:v:425:y:2022:i:c:s0096300322001655
    DOI: 10.1016/j.amc.2022.127081
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    References listed on IDEAS

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    1. Michael Pitt & Sheheryar Malik & Arnaud Doucet, 2014. "Simulated likelihood inference for stochastic volatility models using continuous particle filtering," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 527-552, June.
    2. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
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