In GPS-aided strap-down inertial navigation system, in-motion initial alignment is crucial and can be solved with a closed-loop scheme based on state estimation. With this method, the noise covariance matrices need to be estimated, which, however, can be inaccurate in practice. In this paper, a novel adaptive Kalman filter is proposed to address the above problem. The state and measurement noise covariance matrices are jointly estimated based on a variational Bayesian approach, in which the prior and posterior probability density functions of the state noise covariance matrix and one-step prediction error covariance matrix are assumed to have the same form. Simulation results demonstrate that the proposed algorithm can improve the initial alignment accuracy of the in-motion initial alignment based on a closed-loop scheme as compared with an existing baseline adaptive Kalman filter.


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    Title :

    An Improved Adaptive Kalman Filter for In-motion Initial Alignment of GPS-Aided SINS


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Wang, Botong (author) / Huang, Yulong (author) / Luo, Li (author) / Wang, Wenwu (author) / Zhang, Yonggang (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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