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    Multiple Extended Target Tracking With Labeled Random Finite Sets

    Access Status
    Fulltext not available
    Authors
    Beard, M.
    Reuter, S.
    Granström, K.
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Scheel, A.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Beard, M. and Reuter, S. and Granström, K. and Vo, B. and Vo, B.T. and Scheel, A. 2016. Multiple Extended Target Tracking With Labeled Random Finite Sets. IEEE Transactions on Signal Processing. 64 (7): pp. 1638-1653.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2015.2505683
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP130104404
    URI
    http://hdl.handle.net/20.500.11937/30091
    Collection
    • Curtin Research Publications
    Abstract

    Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the standard measurement model. In this paper, a new algorithm is proposed for tracking multiple extended targets in clutter, which is capable of estimating the number of targets, as well the trajectories of their states, comprising the kinematics, measurement rates, and extents. The proposed technique is based on modeling the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set (RFS), within which the extended targets are modeled using gamma Gaussian inverse Wishart (GGIW) distributions. A cheaper variant of the algorithm is also proposed, based on the labelled multi-Bernoulli (LMB) filter. The proposed GLMB/LMB-based algorithms are compared with an extended target version of the cardinalized probability hypothesis density (CPHD) filter, and simulation results show that the (G)LMB has improved estimation and tracking performance.

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