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    Tracking targets with pairwise-Markov dynamics

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    Fulltext not available
    Authors
    Mahler, Ronald
    Date
    2015
    Collection
    • Curtin Research Publications
    Type
    Conference Paper
    Metadata
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    Abstract

    © 2015 IEEE. Single- and multi-target tracking are both typically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain, observed by an independent observation process. Since HMC independence assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as an approach for weakening them. Petetin and Desbouvries subsequently proposed a PMC generalization of the probability hypothesis density (PHD) filter, but their derivation was somewhat heuristic. The first major purpose of this paper is to construct a solid theoretical foundation for the Petetin-Desbouvries filter - which turns out to be a multitarget HMC model rather than a true multitarget PMC model The second major purpose is to use this foundation to devise PMC versions of any random finite set (RFS) filter, thus allowing tracking of targets with non-HMC dynamics.

    Citation
    Mahler, R. 2015. Tracking targets with pairwise-Markov dynamics, pp. 280-286.
    Source Title
    2015 18th International Conference on Information Fusion, Fusion 2015
    URI
    http://hdl.handle.net/20.500.11937/55996
    Department
    Department of Electrical and Computer Engineering

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