Abstract Multi-target tracking is an important research topic in the field of aerospace. In this paper, a multi-Bernoulli smoother, which consists of forward filtering followed by backward smoothing, is proposed for multi-target tracking. The forward filtering is accomplished by the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter. For the backward smoothing, the smoothed multi-target probability density is approximated by a multi-Bernoulli density, whose backward recursion is derived by using finite set statistics. To solve the computational problem of multiple integrals in the smoother, a sequential Monte Carlo method is also proposed. Experimental results show that the proposed smoother improves the estimation accuracy of target number and target states over the CBMeMBer filter.


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    Title :

    Multi-Bernoulli smoother for multi-target tracking


    Contributors:
    Li, Dong (author) / Hou, Chenping (author) / Yi, Dongyun (author)

    Published in:

    Publication date :

    2015-11-17


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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