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    Robust Multi-target Tracking with Bootstrapped-GLMB Filter

    Do CT 2022 Public.pdf (1.743Mb)
    Access Status
    Open access
    Authors
    Do, Cong-Thanh
    Date
    2022
    Supervisor
    Ba-Ngu Vo
    Ba Tuong Vo
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/88811
    Collection
    • Curtin Theses
    Abstract

    This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters.

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