To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms.


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    Title :

    A New Adaptive Extended Kalman Filter for Cooperative Localization


    Contributors:
    Yulong Huang (author) / Yonggang Zhang (author) / Bo Xu (author) / Zhemin Wu (author) / Chambers, Jonathon A. (author)


    Publication date :

    2018-02-01


    Size :

    1691046 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English