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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    Title :

    Fast data association using multidimensional assignment with clustering


    Contributors:


    Publication date :

    2001-07-01


    Size :

    1076873 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English