The focus of this article is to tackle the challenging task of unresolved-group object (UO) tracking by exploiting the Poisson multi-Bernoulli mixture (PMBM) filter, named UO-PMBM. Specifically, according to the UO likelihood function, the probability generating functional tool and functional derivative are first used to derive the filtering recursion expressions of the UO-PMBM. Then, detailed descriptions of the Gaussian mixture (GM) implementations are described. Lastly, the effectiveness of the proposed UO-PMBM approach is demonstrated through simulation experiments.


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    Title :

    PMBM-Based Unresolved-Group Object Tracking


    Contributors:
    Li, Guchong (author) / Li, Gang (author) / He, You (author)


    Publication date :

    2024-08-01


    Size :

    1359699 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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