Gaussian mixture importance sampling function for unscented SMC-PHD filter

J. Yoon, Du Yong Kim, K. Yoon

    Research output: Contribution to journalArticle

    10 Citations (Scopus)


    The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented information filter is assigned for every particle to approximate an importance sampling function. In this paper, we propose a Gaussian mixture form of the importance sampling function for the SMC-PHD filter to considerably reduce the computational complexity without performance degradation. Simulation results support that the proposed importance sampling function is effective in computational aspects compared with variants of SMC-PHD filters and competitive to the USMC-PHD filter in accuracy. © 2013 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)2664-2670
    JournalIET Signal Processing
    Issue number9
    Publication statusPublished - 2013

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