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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multiple Target Tracking Using Maximum Likelihood Probabilistic Data Association


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.03.2007


    Format / Umfang :

    13273963 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Passive target tracking using maximum likelihood estimation

    Xiao-Jiao Tao / Cai-Rong Zou / Zhen-Ya He | IEEE | 1996





    Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association

    Caveney, D. / Hedrick, J. K. / American Society of Mechanical Engineers | British Library Conference Proceedings | 2001