Kernel size plays a significant role in the performance of the maximum correntropy Kalman filter (MCC-KF). Kernel size is usually chosen by trail and error. If the kernel size is large, the MCC-KF reduces to the Kalman filter (KF). However, if the kernel size is small, the MCC-KF may diverge, or converge slowly. We propose a novel method for adaptive kernel size selection. We calculate kernel size as a weighted sum of the innovation term and the covariance of the filter-indicated estimation error at each time step. We call this filter the "MCC with adaptive kernel size filter" (MCC-AKF). We analytically prove that the true mean square error (TMSE) of the MCC-AKF is less than or equal to that of the MCC-KF under certain conditions. A simulation example is provided to illustrate the analytical results.


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    Title :

    Maximum Correntropy Criterion Kalman Filter with Adaptive Kernel Size


    Contributors:


    Publication date :

    2019-07-01


    Size :

    171734 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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