The Maximum Likelihood-Probabilistic Data Association (MLPDA) target tracking algorithm is effective in tracking very low observable targets. A key limitation of MLPDA is that it is restricted to tracking a single target. We derive and implement a multiple target version of MLPDA called Joint MLPDA (JMLPDA). While the JMLPDA implementation presented in this paper is focused on a two-target case, this algorithm is extensible to any number of targets. The MLPDA and JMLPDA algorithms are combined to form a multi-target MLPDA tracking algorithm. Performance of the JMLPDA and the multi-target MLPDA algorithms are compared to a Probabilistic Multi-Hypothesis Tracker (PMHT) for two crossing targets, focusing on track management/update. Simulation results show that under conditions of heavy clutter, the multi-target MLPDA outperforms PMHT in terms of reduced track errors and longer track life.


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    Title :

    Multiple Target Tracking Using Maximum Likelihood Probabilistic Data Association


    Contributors:


    Publication date :

    2007-03-01


    Size :

    13273963 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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