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    An approximate CPHD filter for superpositional sensors

    Access Status
    Fulltext not available
    Authors
    Mahler, Ronald
    El-Fallah, A.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Mahler, R. and El-Fallah, A. 2012. An approximate CPHD filter for superpositional sensors.
    Source Title
    Proceedings of SPIE - The International Society for Optical Engineering
    DOI
    10.1117/12.975965
    ISBN
    9780819490704
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/56296
    Collection
    • Curtin Research Publications
    Abstract

    Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most familiar sensors, such as surveillance and imaging radars, violate assumption (c). This is because they are actually superpositional-that is, any measurement is a sum of signals generated by all of the targets in the scene. At this conference in 2009, the first author derived exact formulas for PHD and CPHD filters that presume general superpositional measurement models. Unfortunately, these formulas are computationally intractable. In this paper, we modify and generalize a Gaussian approximation technique due to Thouin, Nannuru, and Coates to derive a computationally tractable superpositional-CPHD filter. Implementation requires sequential Monte Carlo (particle filter) techniques. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

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