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    On multitarget pairwise-Markov models

    Access Status
    Fulltext not available
    Authors
    Mahler, Ronald
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Mahler, R. 2015. On multitarget pairwise-Markov models.
    Source Title
    Proceedings of SPIE - The International Society for Optical Engineering
    DOI
    10.1117/12.2177192
    ISBN
    9781628415902
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55796
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 SPIE. Single-and multi-target tracking are both typically based on strong independence assumptions regarding both the target states and sensor measurements. In particular, both are theoretically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain that is observed by an independent observation process. Since HMC assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as a way to weaken those assumptions. In this paper it is shown that the PMC model can be directly generalized to multitarget problems. Since the resulting tracking filters are computationally intractable, the paper investigates generalizations of the cardinalized probability hypothesis density (CPHD) filter to applications with PMC models.

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