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基于递推更新卡尔曼滤波的磁偶极子目标跟踪

吴垣甫 孙跃

吴垣甫, 孙跃. 基于递推更新卡尔曼滤波的磁偶极子目标跟踪[J]. 北京航空航天大学学报, 2017, 43(9): 1805-1812. doi: 10.13700/j.bh.1001-5965.2016.0694
引用本文: 吴垣甫, 孙跃. 基于递推更新卡尔曼滤波的磁偶极子目标跟踪[J]. 北京航空航天大学学报, 2017, 43(9): 1805-1812. doi: 10.13700/j.bh.1001-5965.2016.0694
WU Yuanfu, SUN Yue. Magnetic dipole target tracking based on recursive update Kalman filter[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9): 1805-1812. doi: 10.13700/j.bh.1001-5965.2016.0694(in Chinese)
Citation: WU Yuanfu, SUN Yue. Magnetic dipole target tracking based on recursive update Kalman filter[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9): 1805-1812. doi: 10.13700/j.bh.1001-5965.2016.0694(in Chinese)

基于递推更新卡尔曼滤波的磁偶极子目标跟踪

doi: 10.13700/j.bh.1001-5965.2016.0694
基金项目: 

国家自然科学基金 51509252

详细信息
    作者简介:

    吴垣甫  男, 博士研究生, 工程师; 主要研究方向:目标跟踪、非线性滤波

    孙跃  男, 博士, 教授; 主要研究方向:控制理论与控制工程

    通讯作者:

    孙跃, E-mail:syue06@cqu.edu.cn

  • 中图分类号: TP202

Magnetic dipole target tracking based on recursive update Kalman filter

Funds: 

National Natural Science Foundation of China 51509252

More Information
  • 摘要:

    针对磁性目标跟踪问题,以磁偶极子等效场源模型为基础,建立磁性目标跟踪的离散状态空间模型,将磁偶极子目标实时跟踪问题转化为状态空间模型的滤波估值问题。针对磁性目标初始条件难以获得且现有卡尔曼类滤波算法在大初始误差条件下容易出现发散的问题,提出一种递推观测更新的卡尔曼滤波算法,将现有的一步观测更新描述为递推更新过程,等效降低大初始误差带来的大非线性误差。仿真与实测数据测试结果表明,本文算法具有良好的精度和收敛性,能够有效抑制磁偶极子跟踪中由于大初始误差导致的滤波发散,适于实际应用。

     

  • 图 1  Ψ=π/3时位置和磁矩分量RMSE

    Figure 1.  RMSE of position and magnetic moment component at Ψ=π/3

    图 2  Ψ=π/8时位置和磁矩分量RMSE

    Figure 2.  RMSE of position and magnetic moment component at Ψ=π/8

    图 3  Ψ=π/16时位置和磁矩分量RMSE

    Figure 3.  RMSE of position and magnetic moment component at Ψ=π/16

    图 4  车辆转弯通过测量点

    Figure 4.  Vehicle making turn at measuring position

    图 5  降采样后的运动车辆磁场测量数据

    Figure 5.  Motion vehicle magnetic data after downsampling process

    图 6  不同初值条件下各算法对实测数据的跟踪处理结果

    Figure 6.  Real-world data tracking and processing results by each algorithm under different initializing conditions

    表  1  各算法在不同初值条件下的归一化后验残差平方和

    Table  1.   Normalized posterior residual square sum for each algorithm under different initializing conditions

    算法 r*
    偏离实际初始位置 接近实际初始位置
    EKF 9.363×104 9.285×104
    UKF 1.179×105 9.635×104
    CKF 9.453×105 1.623×107
    PUKF 4.722×105 3.725×103
    PCKF 1.504×105 2.341×106
    RUKF 8.356×104 1.538×103
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-08-30
  • 录用日期:  2016-10-28
  • 网络出版日期:  2017-09-20

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