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基于SRCKF的多传感器融合自适应鲁棒算法

李春辉 马健 杨永建 肖冰松 邓有为

李春辉,马健,杨永建,等. 基于SRCKF的多传感器融合自适应鲁棒算法[J]. 北京航空航天大学学报,2023,49(1):220-228 doi: 10.13700/j.bh.1001-5965.2021.0201
引用本文: 李春辉,马健,杨永建,等. 基于SRCKF的多传感器融合自适应鲁棒算法[J]. 北京航空航天大学学报,2023,49(1):220-228 doi: 10.13700/j.bh.1001-5965.2021.0201
LI C H,MA J,YANG Y J,et al. Adaptively robust multi-sensor fusion algorithm based on square-root cubature Kalman filter[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):220-228 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0201
Citation: LI C H,MA J,YANG Y J,et al. Adaptively robust multi-sensor fusion algorithm based on square-root cubature Kalman filter[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):220-228 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0201

基于SRCKF的多传感器融合自适应鲁棒算法

doi: 10.13700/j.bh.1001-5965.2021.0201
基金项目: 空军工程大学校长基金(XZJ2020039)
详细信息
    通讯作者:

    E-mail:majiankgd@163.com

  • 中图分类号: TN953

Adaptively robust multi-sensor fusion algorithm based on square-root cubature Kalman filter

Funds: Air Force Engineering University President’s Fund (XZJ2020039)
More Information
  • 摘要:

    为解决模型误差和异常量测值发生时平方根容积卡尔曼滤波(SRCKF)算法滤波性能下降甚至滤波发散的问题,提出了一种多传感器融合自适应鲁棒算法。基于新息协方差匹配原则设计了鲁棒子系统以抑制量测异常值,同时为克服模型误差使用基于新息修正的低复杂度自适应SRCKF(LCASRCKF)算法设计了自适应子系统,根据2种子系统的特点和局限提出全局融合架构,使系统可以充分平衡并利用滤波过程中先验的模型预测值信息和后验的量测值信息,最终降低估计误差。仿真结果表明:相比鲁棒多渐消因子容积卡尔曼滤波(RMCKF)等算法,所提融合算法在滤波精度、稳定性和收敛速度等方面有明显优势。

     

  • 图 1  融合架构

    Figure 1.  Fusion architecture

    图 2  目标运动轨迹(场景1)

    Figure 2.  Target trajectory (scenario 1)

    图 3  x方向位置均方根误差(场景1)

    Figure 3.  RMSE of position in x axis (scenario 1)

    图 4  y方向位置均方根误差(场景1)

    Figure 4.  RMSE of position in y axis (scenario 1)

    图 5  x方向位置均方根误差(场景2)

    Figure 5.  RMSE of position in y axis (scenario 2)

    图 6  y方向位置均方根误差(场景2)

    Figure 6.  RMSE of position in y axis (scenario 2)

    图 7  x方向位置RMSE细节(场景2)

    Figure 7.  Details of RMSE of position in x axis (scenario 2)

    图 8  y方向位置RMSE细节(场景2)

    Figure 8.  Details of RMSE of position in y axis (scenario 2)

    表  1  平均RMSE对比(场景1)

    Table  1.   Comparison of mean RMSEs (scenario 1)

    算法x方向位置
    平均RMSE/m
    y方向位置
    平均RMSE/m
    融合算法4.74454.7114
    SRCKF21.009117.7316
    LCASRCKF6.21216.1881
    RMCKF6.58706.3279
    下载: 导出CSV

    表  2  平均RMSE对比(场景2)

    Table  2.   Comparison of mean RMSEs (scenario 2)

    算法x方向位置
    平均RMSE/m
    y方向位置
    平均RMSE/m
    融合算法4.964 34.797 0
    SRCKF94.409 047.955 4
    LCASRCKF67.171 133.293 6
    RMCKF58.766 628.517 5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-20
  • 录用日期:  2021-05-31
  • 刊出日期:  2021-06-03

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