In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for state and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UKF based MAP and based ML, the proposed algorithm provides better convergence and stability.


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    Title :

    A hybrid adaptive unscented Kalman filter algorithm


    Contributors:
    He, Jun (author) / Chen, Yong (author) / Zhang, Zhaoxia (author) / Yin, Wentao (author) / Chen, Danfeng (author)

    Publication date :

    2018-01-01


    Remarks:

    International Journal for Engineering Modelling ; ISSN 1330-1365 (Print) ; ISSN 1849-8671 (Online) ; Volume 31 ; Issue 3


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    518 / 629



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