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    Gaussian mixture importance sampling function for unscented SMC-PHD filter

    Access Status
    Fulltext not available
    Authors
    Yoon, J.
    Kim, Du Yong
    Yoon, K.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Yoon, J. and Kim, D.Y. and Yoon, K. 2013. Gaussian mixture importance sampling function for unscented SMC-PHD filter. Signal Processing. 93 (9): pp. 2664-2670.
    Source Title
    Signal Processing
    DOI
    10.1016/j.sigpro.2013.03.004
    ISSN
    0165-1684
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/56017
    Collection
    • Curtin Research Publications
    Abstract

    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.

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