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    A generalised labelled multi-Bernoulli filter for extended multi-target tracking

    Access Status
    Fulltext not available
    Authors
    Beard, M.
    Reuter, S.
    Granström, K.
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Scheel, A.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Beard, M. and Reuter, S. and Granström, K. and Vo, B. and Vo, B.T. and Scheel, A. 2015. A generalised labelled multi-Bernoulli filter for extended multi-target tracking, in Proceedings of the 18th International Conference on Information Fusion (Fusion), Jul 6-9 2015, pp. 991-998. Washington, DC: IEEE.
    Source Title
    2015 18th International Conference on Information Fusion, Fusion 2015
    ISBN
    9780982443866
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/39950
    Collection
    • Curtin Research Publications
    Abstract

    This paper addresses extended multi-target tracking in clutter, i.e. tracking targets that may produce more than one measurement on each scan. We propose a new algorithm for solving this problem, that is capable of initiating and maintaining labelled estimates of the target kinematics, measurement rates and extents. Our proposed technique is based on modelling the multi-target state as a generalised labelled multi-Bernoulli (GLMB), combined with the gamma Gaussian inverse Wishart (GGIW) distribution for a single extended target. Previously, probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters based on GGIW mixtures have been proposed to solve the extended target tracking problem. Although these are computationally cheaper, they involve significant approximations, as well as lacking the ability to maintain target tracks over time. Here, we compare our proposed GLMB-based approach to the extended target PHD/CPHD filters, and show that the GLMB has improved performance.

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