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    Generalised labelled multi-Bernoulli forward-backward smoothing

    Access Status
    Fulltext not available
    Authors
    Beard, M.
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Beard, M. and Vo, B.T. and Vo, B. 2016. Generalised labelled multi-Bernoulli forward-backward smoothing, in Proceedings of the 19th International Conference on Information Fusion, Jul 5-8 2016, pp. 688-694. Heidelberg, Germany: IEEE.
    Source Title
    FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
    Additional URLs
    http://ieeexplore.ieee.org/abstract/document/7527954/
    ISBN
    9780996452748
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/50665
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB filter, followed by backward propagation of a corrector that is used to obtain the smoothed GLMB density. The smoother is derived under the assumptions of the standard multi-object dynamic model, and the standard multi-object measurement likelihood model, i.e. The proposed smoother is capable of handling an unknown and time-varying number of objects, in the presence of measurement origin uncertainty, clutter, and missed detections.

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