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    Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter

    Access Status
    Fulltext not available
    Authors
    Beard, Michael
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Beard, Michael and Vo, Ba-Tuong and Vo, Ba-Ngu. 2013. Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter. IEEE Signal Processing Letters. 20 (4): pp. 323-326.
    Source Title
    IEEE Signal Processing Letters
    DOI
    10.1109/LSP.2013.2244594
    ISSN
    1070-9908
    URI
    http://hdl.handle.net/20.500.11937/27541
    Collection
    • Curtin Research Publications
    Abstract

    It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (GMCPHD) filter can be used when the clutter density is unknown. Here we examine the performance of this filter, and as one would expect, it does not do as well as the conventional GMCPHD with matched clutter density. To improve the performance, we propose a bootstrap filtering scheme, and demonstrate by simulations on a bearings-only multitarget filtering scenario, that it is capable of performing almost as well as the matched GMCPHD filter.

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