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Fuzzy Quadrature Particle Filter for Maneuvering Target Tracking

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Abstract

In this paper, a novel fuzzy quadrature particle filter (FQPF) based on maximum entropy fuzzy clustering for maneuvering target tracking is proposed. The novelties of the fuzzy quadrature particle filter are in the update step in which the predicted and posterior probability density functions are approximated by introducing a set of quadrature point probability densities based on the Gauss–Hermite quadrature rule as a Gaussian. The particle and quadrature point weights can be adaptively estimated based on the weighting exponent and fuzzy membership degrees provided by a modified version of maximum entropy fuzzy clustering algorithm. Unlike the Gaussian particle filter (GPF) using the prior distribution as the proposal distribution, the new FQPF uses a set of modified quadrature point probability densities as the proposal distribution that can effectively enhance the diversity of samples and improve the approximate performance. Finally, simulation results are presented to demonstrate the versatility and improved performance of the fuzzy quadrature particle filter over other nonlinear filtering approaches, namely the unscented Kalman filter, quadrature Kalman filter, particle filter, and GPF, to solve maneuvering target tracking problems.

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Acknowledgments

The authors would like to thank the editor and all anonymous reviewers for their valuable comments. This work was supported by the National Natural Science Foundation of China (61301074, 61271107, 61375015), Natural Science Foundation of the Guangdong Province of China (S2012010009417), Key Project of National Science & technology of pillar program (2011BAH24B12), Science & Technology Program of Shenzhen (No. JCYJ 20130329105816574, JCYJ20140418095735618), and Defense Advanced Research Fund Project (91400C800501140C80340).

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Authors and Affiliations

  1. School of Information Engineering, Shenzhen University, Shenzhen, Box 2-603, 518060, GuangDong, People’s Republic of China

    Liang-qun Li, Chun-lan Li, Wen-ming Cao & Zong-Xiang Liu

  2. ATR Key Laboratory, Shenzhen University, Shenzhen, 518060, GuangDong, People’s Republic of China

    Liang-qun Li, Chun-lan Li & Zong-Xiang Liu

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  1. Liang-qun Li
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  2. Chun-lan Li
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  4. Zong-Xiang Liu
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Correspondence to Liang-qun Li.

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Li, Lq., Li, Cl., Cao, Wm. et al. Fuzzy Quadrature Particle Filter for Maneuvering Target Tracking. Int. J. Fuzzy Syst. 18, 647–658 (2016). https://doi.org/10.1007/s40815-015-0105-5

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  • DOI: https://doi.org/10.1007/s40815-015-0105-5

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