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基于最小化新息协方差的修正SRCKF算法

杨永建 甘轶 李春辉 邓有为 肖冰松 彭芳

杨永建,甘轶,李春辉,等. 基于最小化新息协方差的修正SRCKF算法[J]. 北京航空航天大学学报,2023,49(1):138-144 doi: 10.13700/j.bh.1001-5965.2021.0202
引用本文: 杨永建,甘轶,李春辉,等. 基于最小化新息协方差的修正SRCKF算法[J]. 北京航空航天大学学报,2023,49(1):138-144 doi: 10.13700/j.bh.1001-5965.2021.0202
YANG Y J,GAN Y,LI C H,et al. Amended SRCKF algorithm based on minimum variance of innovation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):138-144 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0202
Citation: YANG Y J,GAN Y,LI C H,et al. Amended SRCKF algorithm based on minimum variance of innovation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):138-144 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0202

基于最小化新息协方差的修正SRCKF算法

doi: 10.13700/j.bh.1001-5965.2021.0202
详细信息
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    E-mail:929652284@qq.com

  • 中图分类号: TP273

Amended SRCKF algorithm based on minimum variance of innovation

More Information
  • 摘要:

    目标跟踪过程中的模型误差会使得平方根容积卡尔曼滤波(SRCKF)性能下降,滤波精度降低;自适应滤波中的修正卡尔曼滤波(AKF)算法可以有效解决这一问题,但是难以应用到非线性滤波中。为了克服模型误差带来的不利影响,同时,进一步提高修正思想的应用范围,在SRCKF的基础上,基于最小化新息协方差准则推导了修正系数的向量形式,提出修正SRCKF(ASRCKF)算法。所提算法通过利用后期的测量数据,增加对测量值的信任度,从而达到对目标模型误差进行补偿的目的。仿真结果表明:与SRCKF和强跟踪SRCKF算法相比,所提ASRCKF算法能有效抑制模型误差,有着更优的滤波性能。

     

  • 图 1  算法流程

    Figure 1.  Flow chart of the algorithm

    图 2  场景1位置均方根误差

    Figure 2.  RMSE of position in case 1

    图 3  场景1速度均方根误差

    Figure 3.  RMSE of velocity in case 1

    图 4  场景1加速度均方根误差

    Figure 4.  RMSE of acceleration in case 1

    图 5  场景2位置均方根误差

    Figure 5.  RMSE of position in case 2

    图 6  场景2速度均方根误差

    Figure 6.  RMSE of velocity in case 2

    图 7  场景2加速度均方根误差

    Figure 7.  RMSE of acceleration in case 2

    表  1  场景1误差均值对比

    Table  1.   Comparison of mean error for each algorithm in case 1

    算法位置误差
    均值/m
    速度误差
    均值/(m·s−1)
    加速度误差
    均值/(m·s−2)
    SRCKF7.970 76.984 22.808 3
    ASRCKF4.752 04.119 12.320 1
    STF-SRCKF7.520 714.643 510.654 1
    下载: 导出CSV

    表  2  场景2误差均值对比

    Table  2.   Comparison of mean error for each algorithm in case 2

    算法位置误差
    均值/m
    速度误差
    均值/(m·s−1)
    加速度误差
    均值/(m·s−2)
    SRCKF12.517513.11145.6948
    ASRCKF6.43686.71864.2920
    STF-SRCKF8.292416.196112.1711
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-20
  • 录用日期:  2021-08-22
  • 网络出版日期:  2023-01-16
  • 刊出日期:  2021-09-14

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