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    A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update

    Access Status
    Fulltext not available
    Authors
    Hoang, H.
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Hoang, H. and Vo, B. and Vo, B.T. 2015. A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update, in Proceedings of the 18th International Conference on Information Fusion (Fusion), Jul 6-9 2015, pp. 999-1006. Washington, DC: IEEE. pp. 999-1006.
    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/20.500.11937/35397
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a new implementation for the delta generalized labeled multi-Bernoulli (d-GLMB) filter by combining prediction and update into a single step. In contrast to the original implementation which requires different truncation procedures for each component in the prediction and update, the joint strategy involves only one truncation per component in the filtering density, thus drastically reduces the number of computations. Performance comparison with the original implementation is presented through numerical studies.

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