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    Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization

    Access Status
    Fulltext not available
    Authors
    Garcia-Fernandez, Angel
    Vo, Ba-Ngu
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Garcia-Fernandez, A. and Vo, B. 2015. Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization. IEEE Transactions on Signal Processing. 63 (21): pp. 5812-5820.
    Source Title
    IEEE TRANSACTIONS ON SIGNAL PROCESSING
    DOI
    10.1109/TSP.2015.2468677
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP130104404
    URI
    http://hdl.handle.net/20.500.11937/24733
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show that both the PHD and CPHD filters fit in the context of assumed density filtering and implicitly perform Kullback-Leibler divergence (KLD) minimizations after the prediction and update steps. We perform the KLD minimizations directly on the multitarget prediction and posterior densities.

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