In a Bayesian framework, we propose a hierarchy of suboptimal retrodiction algorithms that generalize Rauch-Tung-Striebel (RTS) fixed-interval smoothing to multiple hypothesis tracking (MHT) applications employing interacting multiple model (IMM) methods (IMM-MHT). As a limiting case we obtain new simple formulae for suboptimal fixed-interval smoothing applied to Markovian switching systems. Retrodiction techniques provide uniquely interpretable and accurate trajectories from ambiguous MHT output if a certain (small) time delay is tolerated. By a simulated example with two maneuvering targets that operate closely spaced under relatively hard conditions we demonstrate the potential gain by fixed-interval retrodiction and provide a quantitative idea of the achievable track accuracy and mean time delay involved.


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    Title :

    Fixed-interval retrodiction approach to Bayesian IMM-MHT for maneuvering multiple targets


    Contributors:
    Koch, W. (author)


    Publication date :

    2000-01-01


    Size :

    388775 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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