Particle filter approaches for approximating the first-order moment of a joint, or probability hypothesis density (PHD), have demonstrated a feasible suboptimal method for tracking a time-varying number of targets in real-time. We consider two techniques for estimating the target states at each iteration, namely k-means clustering and mixture modelling via the expectation-maximization (EM) algorithm. We present novel techniques for associating the targets between frames to enable track continuity.


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    Title :

    Multi-target state estimation and track continuity for the particle PHD filter


    Contributors:
    Clark, D.E. (author) / Bell, J. (author)


    Publication date :

    2007-10-01


    Size :

    1860437 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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