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Improved Maximum Likelihood Filter Based on UD Decomposition Algorithm and its Application in Transfer Alignment

Wei ZhangGuojin PengBingnan YuanPeng WangZhaohui HuoZhe Yang

Chinese Flight Test Establishment

摘要:For the problem that the statistical characteristics of noise is difficult to accurately determine, and meantime the accumulation of calculation error will cause the state estimation covariance matrix to lose positive definiteness in the process of transfer alignment(TA), this paper proposes an improved maximum likelihood adaptive Kalman filter(AKF) based on UD decomposition algorithm. Firstly, real-time estimators of system noise and measurement noise according to the maximum likelihood criterion are constructed, and the observation information is used to update and correct the statistical characteristics of the noise in real time; Secondly, the UD decomposition algorithm is performed on the one-step prediction error covariance matrix, and the decomposed matrixes are updated with time to ensure the symmetric positive definiteness of the covariance matrix. The improved algorithm is applied to TA and compared with the traditional maximum likelihood filter. The simulation results show that the improved algorithm can maintain the noise adaptive ability. At the same time, when the prior state covariance matrix is unknown, the numerical stability of the filtering process can be effectively improved and the fast TA can be achieved.
会议名称:

第三十八届中国控制会议

会议时间:

2019-07-27

会议地点:

中国广东广州

  • 专辑:

    信息科技

  • 专题:

    无线电电子学; 电信技术

  • 分类号:

    TN96;TN713

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