In this paper, we derive a modified interacting multiple model filter for jump Markov systems with unknown process and measurement noise covariances. Using the inverse-Wishart distribution as the conjugate prior of noise covariances, the system state together with the noise parameters for each mode are inferred by the variational Bayesian method. The mixing and output estimates are calculated according to the weighted Kullback–Leibler average of mode-conditioned estimates. Simulation results show the effectiveness of the proposed algorithm.


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    Title :

    Variational Bayesian IMM-Filter for JMSs With Unknown Noise Covariances


    Contributors:


    Publication date :

    2020-04-01


    Size :

    1000197 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English