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Multitarget state and track estimation for the probability hypotheses density filter

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Journal of Electronics (China)

Abstract

The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the estimates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) approaches are used for the FMM parameter estimation.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Weifeng Liu, Chongzhao Han, Feng Lian, Xiaobin Xu & Chenglin Wen

  2. Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China

    Xiaobin Xu & Chenglin Wen

Authors
  1. Weifeng Liu
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  2. Chongzhao Han
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  3. Feng Lian
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  4. Xiaobin Xu
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  5. Chenglin Wen
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Corresponding author

Correspondence to Weifeng Liu.

Additional information

Supported by the National Key Fundamental Research & Development Program of China (2007CB11006) and the Zhejiang Natural Science Foundation (R106745, Y1080422).

Communication author: Liu Weifeng, born in 1973, male, Ph.D. candidate.

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Liu, W., Han, C., Lian, F. et al. Multitarget state and track estimation for the probability hypotheses density filter. J. Electron.(China) 26, 2–12 (2009). https://doi.org/10.1007/s11767-008-0120-x

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  • DOI: https://doi.org/10.1007/s11767-008-0120-x

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