For the multi-sensor discrete linear time-invariant random system, the optimal covariance intersection (CI) fusion steady-state Kalman filters are presented, which have uncharted cross-covariance among the partial filtering errors. Their accuracies are higher than those of the partial optimal steady-state Kalman filters, and are lower than those of the optimal fusion Kalman filters which are fused by the cross-covariances. In the case that both the cross-covariance and the noise variances are uncharted, substituting the online consistent estimators of the noise variances into the optimal CI fusion Kalman filter, a self-correcting CI fusion Kalman filter is presented. I have proven its optimality asymptotically by the method of DESA (dynamic error system analysis) and the continuous properties of functions, i.e. the self-correcting CI Kalman fuser convergences to the optimal CI fuser in a implementation. One Monte-Carlo emulation example verifies the precision grade among the partial and fusion Kalman estimators, and the convergence of the self-correcting Kalman fuser.


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    Title :

    Optimal and Self-correcting Covariance Intersection Fusion Kalman Filters


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Yi (editor) / Martinsen, Kristian (editor) / Yu, Tao (editor) / Wang, Kesheng (editor) / Zhang, Peng (author) / Liu, Jinfang (author)

    Conference:

    International Workshop of Advanced Manufacturing and Automation ; 2020 ; Zhanjiang, China October 12, 2020 - October 13, 2020



    Publication date :

    2021-01-23


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English







    Information Fusion based on Fast Covariance Intersection Filtering

    Niehsen, W. / International Society of Information Fusion / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2002