For single-target multisensor systems, two fusion methods are presented for distributed recursive state estimation of dynamic systems without knowledge of noise covariances. The estimator at every local sensor embeds the dynamics and the forgetting factor into the recursive least squares (RLS) method to remedy the lack of knowledge of noise statistics, developed before as the extended forgetting factor recursive least squares (EFRLS) estimator. It is proved that the two fusion methods are equivalent to the centralized EFRLS that uses all measurements from local sensors directly and their good performance is shown by simulation examples.


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    Title :

    Fusion of distributed extended forgetting factor RLS state estimators


    Contributors:
    Yunmin Zhu, (author) / Keshu Zhang, (author) / Li, X.R. (author)


    Publication date :

    2008-04-01


    Size :

    2302547 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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