We present a fast data association technique based on clustering and multidimensional assignment algorithms for multisensor-multitarget tracking Assignment-based methods have been shown to be very effective for data association. Multidimensional assignment for data association is an NP-hard problem and various near-optimal modifications with (pseudo-)polynomial complexity have been proposed. In multidimensional assignment, candidate assignment tree building consumes about 95% of the time. We present the development of a fast data association algorithm, which partitions the problem into smaller sub-problems. A clustering approach, which attempts to group measurements before forming the candidate tree, is developed for various target-sensor configurations. Simulation results show significant computational savings over the standard multidimensional assignment approach without clustering.


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    Titel :

    Fast data association using multidimensional assignment with clustering


    Beteiligte:
    Chummun, M.R. (Autor:in) / Kirubarajan, T. (Autor:in) / Pattipati, K.R. (Autor:in) / Bar-Shalom, Y. (Autor:in)


    Erscheinungsdatum :

    01.07.2001


    Format / Umfang :

    1076873 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch