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    'Statistics 102' for multisource-multitarget detection and tracking

    Access Status
    Fulltext not available
    Authors
    Mahler, Ronald
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Mahler, R. 2013. 'Statistics 102' for multisource-multitarget detection and tracking. IEEE Journal on Selected Topics in Signal Processing. 7 (3): pp. 376-389.
    Source Title
    IEEE Journal on Selected Topics in Signal Processing
    DOI
    10.1109/JSTSP.2013.2253084
    ISSN
    1932-4553
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/56146
    Collection
    • Curtin Research Publications
    Abstract

    This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, 'top-down,' direct generalization of ordinary single-sensor, single-target engineering statistics to the realm of multisensor, multitarget detection and tracking. Finite-set statistics provides powerful new conceptual and computational methods for dealing with multisensor-multitarget detection and tracking problems. The paper describes how 'multitarget integro-differential calculus' is used to extend conventional single-sensor, single-target formal Bayesian motion and measurement modeling to general tracking problems. Given such models, the paper describes the Bayes-optimal approach to multisensor-multitarget detection and tracking: the multisensor-multitarget recursive Bayes filter. Finally, it describes how multitarget calculus is used to derive principled statistical approximations of this optimal filter, such as PHD filters, CPHD filters, and multi-Bernoulli filters. © 2007-2012 IEEE.

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