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    Computationally-tractable approximate PHD and CPHD filters for superpositional sensors

    Access Status
    Fulltext not available
    Authors
    Nannuru, S.
    Coates, M.
    Mahler, Ronald
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Nannuru, S. and Coates, M. and Mahler, R. 2013. Computationally-tractable approximate PHD and CPHD filters for superpositional sensors. IEEE Journal on Selected Topics in Signal Processing. 7 (3): pp. 410-420.
    Source Title
    IEEE Journal on Selected Topics in Signal Processing
    DOI
    10.1109/JSTSP.2013.2251605
    ISSN
    1932-4553
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55359
    Collection
    • Curtin Research Publications
    Abstract

    In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations. As an example, we present simulation experiments that involve tracking multiple targets using acoustic amplitude sensors and a radio-frequency tomography sensor system. Our simulation study indicates that the CPHD filter provides promising tracking accuracy with reasonable computational requirements. © 2007-2012 IEEE.

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