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多模型自校准无迹Kalman滤波方法

杨海峰 王宇翔

杨海峰, 王宇翔. 多模型自校准无迹Kalman滤波方法[J]. 航空动力学报, 2024, 39(8):20220516 doi: 10.13224/j.cnki.jasp.20220516
引用本文: 杨海峰, 王宇翔. 多模型自校准无迹Kalman滤波方法[J]. 航空动力学报, 2024, 39(8):20220516 doi: 10.13224/j.cnki.jasp.20220516
YANG Haifeng, WANG Yuxiang. Multiple-model self-calibration unscented Kalman filter method[J]. Journal of Aerospace Power, 2024, 39(8):20220516 doi: 10.13224/j.cnki.jasp.20220516
Citation: YANG Haifeng, WANG Yuxiang. Multiple-model self-calibration unscented Kalman filter method[J]. Journal of Aerospace Power, 2024, 39(8):20220516 doi: 10.13224/j.cnki.jasp.20220516

多模型自校准无迹Kalman滤波方法

doi: 10.13224/j.cnki.jasp.20220516
基金项目: 国家自然科学基金面上项目(61972021)
详细信息
    作者简介:

    杨海峰(1993-),男,博士,主要从事滤波算法、自主导航、深空探测等方面的研究。E-mail:halfyang@buaa.edu.cn

  • 中图分类号: V448;O231

Multiple-model self-calibration unscented Kalman filter method

  • 摘要:

    基于无迹Kalman滤波方法(UKF)、自校准无迹Kalman滤波方法(SUKF)和多模型估计理论(MME),针对工程实际中强非线性系统状态方程受未知输入(如医用机械臂惯导单元的零漂误差、列车行驶中遭遇突风和机载元器件故障等)影响的问题,提出了一种多模型自校准无迹Kalman滤波方法(MSUKF),将多模型自校准Kalman滤波方法(MSKF)的适用范围扩展到了强非线性领域。该方法同时采用UKF与SUKF进行计算,根据贝叶斯定理实时分配两者先验估计值的权重,通过加权融合进而得到最终的状态估计。大量数值仿真结果表明:本文方法精度比滤波发散的UKF提高了50%,与无偏的SUKF相比也提升了4%以上,具有更强的适应性和鲁棒性。

     

  • 图 1  模型概率比较

    Figure 1.  Comparison of model probability

    图 2  状态误差比较

    Figure 2.  Comparison of state error

    图 3  UKF、SUKF和MSUKF方法的方均根误差比较

    Figure 3.  Comparison of root mean square error of UKF,SUKF and MSUKF methods

    图 4  UKF和SUKF方法概率均值比较

    Figure 4.  Comparison of probability mean of UKF and SUKF methods

    表  1  UKF、SEKF和MSEKF方法的方均根误差均值

    Table  1.   Mean of root mean square error of UKF,SUKF and MSUKF methods

    方法方均根误差均值
    MSUKF0.086
    SUKF0.090
    UKF0.173
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-19
  • 网络出版日期:  2024-02-28

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