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    CPHD Filtering With Unknown Clutter Rate and Detection Profile

    Access Status
    Fulltext not available
    Authors
    Mahler, R.
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Mahler, R. and Vo, B.T. and Vo, B. 2011. CPHD Filtering With Unknown Clutter Rate and Detection Profile. IEEE Transactions on Signal Processing. 59 (8): pp. 3497-3513.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2011.2128316
    ISSN
    1053-587X
    URI
    http://hdl.handle.net/20.500.11937/6872
    Collection
    • Curtin Research Publications
    Abstract

    In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions.

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      In Bayesian multi-target filtering we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in ...
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