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    RTS Smoother for GLMB filter

    92845.pdf (817.6Kb)
    Access Status
    Open access
    Authors
    Nguyen, Tran Thien Dat
    Yu, J.
    Date
    2019
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nguyen, T.T.D. and Yu, J. 2019. RTS Smoother for GLMB filter. In 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Chengdu, China.
    Source Title
    ICCAIS 2019 - 8th International Conference on Control, Automation and Information Sciences
    DOI
    10.1109/ICCAIS46528.2019.9074579
    ISBN
    9781728123110
    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/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/93021
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we implement a low-cost but effective smoothing strategy to smooth estimated tracks returned by the GLMB filter. While the forward filtering step is carried out via the GLMB filtering procedure, the backward smoothing step is recursively implemented from the final time step to the first time step via a smoothing algorithm. In particular, the smoothing algorithm is based on the Rauch-Tung-Striebel (RTS) of fixed-interval smoother. We demonstrate our smoothing strategy on a linear Gaussian model and the experimental results show consistent improved tracking performance over 100 Monte Carlo runs.

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