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    Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects

    Access Status
    Fulltext not available
    Authors
    Nguyen, Hoa
    Rezatofighi, H.
    Vo, Ba-Ngu
    Ranasinghe, D.C.
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Nguyen, H.V. and Rezatofighi, H. and Vo, B.N. and Ranasinghe, D.C. 2019. Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects. IEEE Transactions on Signal Processing. 67 (20): pp. 5365-5379.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2019.2939076
    Additional URLs
    https://arxiv.org/abs/1808.04445
    ISSN
    1053-587X
    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/20.500.11937/91028
    Collection
    • Curtin Research Publications
    Abstract

    We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.

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