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    Bayesian multi-target tracking with merged measurements using labelled random finite sets

    Access Status
    Fulltext not available
    Authors
    Beard, M.
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Beard, M. and Vo, B.T. and Vo, B. 2015. Bayesian multi-target tracking with merged measurements using labelled random finite sets. Transactions on Signal Processing. 63 (6): pp. 1433-1447.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2015.2393843
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/28547
    Collection
    • Curtin Research Publications
    Abstract

    Most tracking algorithms in the literature assume that the targets always generate measurements independently of each other, i.e., the sensor is assumed to have infinite resolution. Such algorithms have been dominant because addressing the presence of merged measurements increases the computational complexity of the tracking problem, and limitations on computing resources often make this infeasible. When merging occurs, these algorithms suffer degraded performance, often due to tracks being terminated too early. In this paper, we use the theory of random finite sets (RFS) to develop a principled Bayesian solution to tracking an unknown and variable number of targets in the presence of merged measurements. We propose two tractable implementations of the resulting filter, with differing computational requirements. The performance of these algorithms is demonstrated by Monte Carlo simulations of a multi-target bearings-only scenario, where measurements become merged due to the effect of finite sensor resolution.

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