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    Multi-target Track-Before-Detect using labeled random finite set

    Access Status
    Fulltext not available
    Authors
    Papi, F.
    Vo, Ba Tuong
    Bocquel, M.
    Vo, Ba-Ngu
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Papi, Francesco and Vo, Ba-Tuong and Bocquel, Melanie and Vo, Ba-Ngu. 2013. Multi-target Track-Before-Detect using labeled random finite set, in International Conference on Control, Automation and Information Sciences (ICCAIS), Nov 25-28 2013, pp. 116-121. Nha Trang, Vietnam: IEEE.
    Source Title
    2013 International Conference on Control, Automation and Information Sciences
    Source Conference
    ICCAIS 2013
    DOI
    10.1109/ICCAIS.2013.6720540
    ISBN
    978-1-4799-0569-0
    URI
    http://hdl.handle.net/20.500.11937/26035
    Collection
    • Curtin Research Publications
    Abstract

    Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance. In this case an approach called Track-Before-Detect (TBD) that operates on the pre-detection signal, is needed. In this paper we present a labeled random finite set solution to the multitarget TBD problem. To the best of our knowledge this is the first provably Bayes optimal approach to multi-target tracking using image data. Simulation results using realistic radar-based TBD scenarios are also presented to demonstrate the capability of the proposed approach.

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