In this paper, the problem of unknown clutter prior information for multiple extended targets is considered by introducing the network flow theory. To achieve multiple extended target tracking using this technique, a hierarchical clustering algorithm is developed to segment the measurement set. In particular, we build a multiple extended target (MET) network flow model with a multi-constrained minimum cost optimization function, as well as use the A-star search algorithm to obtain optimal associated tracks. The proposed algorithm is compared with an extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter, and the simulation results show that the proposed method has improved estimation and tracking performance.


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    Title :

    Multi-extended Target Tracking Algorithm with Unknown Clutter Information


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Zhang, Qi (author) / Ma, Tianli (author) / Gao, Song (author) / Chen, Chaobo (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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