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    The Smooth Trajectory Estimator for LMB Filters

    96260.pdf (696.5Kb)
    Access Status
    Open access
    Authors
    Nguyen, Hoa Van
    Nguyen, Tran Thien Dat
    Shim, Changbeom
    Anuar, M.
    Date
    2023
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nguyen, H.V. and Nguyen, T.T.D. and Shim, C. and Anuar, M. 2023. The Smooth Trajectory Estimator for LMB Filters. In Proceedings of 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS), 27-29 Nov 2023, Hanoi, Vietnam.
    Source Title
    Proceedings - 12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023
    Source Conference
    2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS)
    DOI
    10.1109/ICCAIS59597.2023.10382267
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP200301507
    URI
    http://hdl.handle.net/20.500.11937/96496
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a smooth-trajectory estimator (i.e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time. Experimental results under two challenging scenarios demonstrate significant tracking accuracy improvements with negligible additional computational time compared to the conventional LMB filter.

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