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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    Title :

    Distributed Multi-Target Tracking with D-DBSCAN Clustering


    Contributors:


    Publication date :

    2019-11-01


    Size :

    752491 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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