The probabilistic multiple hypothesis tracker (PMHT) uses the expectation-maximization (EM) algorithm to solve the measurement-origin uncertainty problem. Here, we explore some of its variants for maneuvering targets and in particular discuss the multiple model PMHT. We apply this PMHT to the six "typical" tracking scenarios given in the second benchmark problem from W. D. Blair and G. A. Watson (1998). The manner in which the PMHT is used to track the targets and to manage radar allocation is discussed, and the results compared with those of the interacting multiple model probabilistic data association filter (IMM/PDAF) and IMM/MHT (multiple hypothesis tracker). The PMHT works well: its performance lies between those of the IMM/PDAF and IMM/MHT both in terms of tracking performance and computational load.


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    Title :

    Multiple model PMHT and its application to the benchmark radar tracking problem


    Contributors:
    Ruan, Y. (author) / Willett, P. (author)


    Publication date :

    2004-10-01


    Size :

    929398 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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