We address the problem of multitarget tracking (MTT) encountered in many situations in signal or image processing. We consider stochastic dynamic systems detected by observation processes. The difficulty lies in the fact that the estimation of the states requires the assignment of the observations to the multiple targets. We propose an extension of the classical particle filter where the stochastic vector of assignment is estimated by a Gibbs sampler. This algorithm is used to estimate the trajectories of multiple targets from their noisy bearings, thus showing its ability to solve the data association problem. Moreover this algorithm is easily extended to multireceiver observations where the receivers can produce measurements of various nature with different frequencies.


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    Title :

    Tracking multiple objects with particle filtering


    Contributors:
    Hue, C. (author) / Le Cadre, J.-P. (author) / Perez, P. (author)


    Publication date :

    2002-07-01


    Size :

    1306682 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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