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    Efficient importance sampling function design for sequential Monte Carlo PHD filter

    Access Status
    Fulltext not available
    Authors
    Hong Yoon, J.
    Kim, Du Yong
    Yoon, K.
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Hong Yoon, J. and Kim, D.Y. and Yoon, K. 2012. Efficient importance sampling function design for sequential Monte Carlo PHD filter. Signal Processing. 92 (9): pp. 2315-2321.
    Source Title
    Signal Processing
    DOI
    10.1016/j.sigpro.2012.01.010
    ISSN
    0165-1684
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/56383
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function. Even though this filter permits general class of IS density function, many previous implementations have simply used the state transition density function. However, this approach leads to a degeneracy problem and renders the filter inefficient. Thus, we propose a novel IS function for the SMC-PHD filter using a combination of an unscented information filter and a gating technique. Further, we use measurement-driven birth target intensities because they are more efficient and accurate than selecting birth targets selected using arbitrary or expected mean target states. The performance of the SMC-PHD filter with the proposed IS function was subsequently evaluated through a simulation and it was shown to outperform the standard SMC-PHD filter and recently proposed auxiliary PHD filter. © 2012 Elsevier B.V. All rights reserved.

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