This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.


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    Title :

    Extended Target Tracking using a Gaussian-Mixture PHD Filter


    Contributors:
    Granstrom, K. (author) / Lundquist, C. (author) / Orguner, O. (author)


    Publication date :

    2012-10-01


    Size :

    4369227 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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