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    Forward-Backward Probability Hypothesis Density Smoothing

    Access Status
    Fulltext not available
    Authors
    Mahler, R.
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Mahler, R. and Vo, B.T. and Vo, B. 2012. Forward-Backward Probability Hypothesis Density Smoothing. IEEE Transactions on Aerospace and Electronic Systems. 48 (1): pp. 707-728.
    Source Title
    IEEE Transactions on Aerospace and Electronic Systems
    DOI
    10.1109/TAES.2012.6129665
    ISSN
    00189251
    URI
    http://hdl.handle.net/20.500.11937/47396
    Collection
    • Curtin Research Publications
    Abstract

    A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using finite set statistics (FISST) and standard point process theory. Unlike the forward PHD recursion, the proposed backward PHD recursion is exact and does not require the previous iterate to be Poisson. In addition, assuming the previous iterate is Poisson, the cardinality distribution and all moments of the backward-smoothed multi-target density are derived. It is also shown that PHD smoothing alone does not necessarily improve cardinality estimation. Using an appropriate particle implementation we present a number of experiments to investigate the ability of the proposed multi-target smoother to correct state as well as cardinality errors.

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