This paper proposes a novel clustering-based distributed multi-target tracking algorithm over a sensor network. Each local sensor runs a joint probabilistic data association filter to obtain local state estimation. The estimates are communicated between connected sensors for track-totrack association and fusion. A novel distributed DBSCAN (D-DBSCAN) clustering algorithm is proposed to solve the track-to-track association problem. The proposed algorithm shows advantages in computational efficiency compared with conventional distributed multi-target tracking approaches. Extensive simulations provided substantial evidence for the effectiveness of the proposed algorithm.


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    Titel :

    Distributed Multi-Target Tracking with D-DBSCAN Clustering


    Beteiligte:
    Xu, Shuoyuan (Autor:in) / Shin, Hyo-Sang (Autor:in) / Tsourdos, Antonios (Autor:in)


    Erscheinungsdatum :

    2019-11-01


    Format / Umfang :

    752491 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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