Due to the statistical property of measurement noise varying from time and outliers in engineering applications, the standard Kalman filter is oscillating or even divergent. To solve this problem, a new optimal method is proposed. The measurement covariance is estimated more precisely in time by a replacement of a posteriori covariance at last step with a priori covariance which contains more current information. A novel three-segment function allowing to simultaneously restrain the outliers and tune the a posteriori covariance is presented. The experimental results show that the proposed method outperforms the common robust adaptive filter.


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    Title :

    Optimized Kalman Filter Approach with Innovation-based Outlier Diagnosis


    Contributors:
    Ge, Baoshuang (author) / Zhang, Hai (author) / Sheng, Wei (author) / Chen, Jieling (author)


    Publication date :

    2018-08-01


    Size :

    369400 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English