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    Tracking 'bunching' multitarget correlations

    Access Status
    Fulltext not available
    Authors
    Mahler, Ronald
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Mahler, R. 2015. Tracking 'bunching' multitarget correlations, pp. 102-109.
    Source Title
    IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
    DOI
    10.1109/MFI.2015.7295793
    ISBN
    9781479977727
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55757
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 IEEE. In point process theory, permanental processes are used to model statistical populations whose members tend to be attracted to each other ('bunch'). This paper initiates what appears to be the first application of permanental processes to multitarget detection and tracking. Permanental processes can be used to construct bivariate-Poisson models of statistical correlations between two Poisson multitarget populations. We introduce a recursive Bayes filter for such permanentally-correlated multitarget systems. Then, by analogy with the probability hypothesis density (PHD) filter, we derive first-order approximate filter equations. This permanental-PHD filter requires the (removable) assumption that probability of detection is unity.

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