We present an information theoretic approach to develop an interacting multiple model (IMM) estimator. In the mixing and output steps of the proposed estimator, the weighted Kullback-Leibler (KL) divergence is used to derive the fusion of conditional probability density functions. A lower bound and an upper bound are derived for the error covariance of controllable and observable Markov jump linear systems. Simulation results are provided to verify the effectiveness of the proposed estimator.


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    Title :

    An information theoretic approach to interacting multiple model estimation


    Contributors:
    Wenling Li, (author) / Yingmin Jia, (author)


    Publication date :

    2015-07-01


    Size :

    1394108 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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