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    An overview of particle methods for random finite set models

    Access Status
    Fulltext not available
    Authors
    Ristic, B.
    Beard, Michael
    Fantacci, C.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Ristic, B. and Beard, M. and Fantacci, C. 2016. An overview of particle methods for random finite set models. Information fusion. 31: pp. 110-126.
    Source Title
    Information fusion
    DOI
    10.1016/j.inffus.2016.02.004
    ISSN
    1566-2535
    School
    School of Electrical Engineering and Computing
    URI
    http://hdl.handle.net/20.500.11937/52414
    Collection
    • Curtin Research Publications
    Abstract

    This overview paper describes the particle methods developed for the implementation of the class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application.

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