北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (9): 1805-1812.doi: 10.13700/j.bh.1001-5965.2016.0694

• 论文 • 上一篇    下一篇

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   

  1. 重庆大学 自动化学院, 重庆 400044
  • 收稿日期:2016-08-30 出版日期:2017-09-20 发布日期:2016-11-29
  • 通讯作者: 孙跃,E-mail:syue06@cqu.edu.cn E-mail:syue06@cqu.edu.cn
  • 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
  • 基金资助:
    国家自然科学基金(51509252)

Magnetic dipole target tracking based on recursive update Kalman filter

WU Yuanfu, SUN Yue   

  1. School of Automation, Chongqing University, Chongqing 400044, China
  • Received:2016-08-30 Online:2017-09-20 Published:2016-11-29
  • Supported by:
    National Natural Science Foundation of China (51509252)

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。

关键词: 磁偶极子, 跟踪, 非线性滤波, 线性化, 卡尔曼滤波器

Abstract: The magnetic target tracking problem is addressed in this paper by establishing the discrete state-space model on the basis of the equivalent magnetic dipole model in order to formulate the real-time magnetic dipole target tracking problem as filtering estimation problem of state-space model. Then a novel filtering approach with the recursive update process is proposed to address the divergence problem of magnetic target tracking under large prior error condition when using present Kalman-type filters. The one-step measurement update is replaced by recursive update process; hence the large nonlinearized error caused by large prior error is reduced in each recursive step. The proposed algorithm is tested by simulation and real-world magnetic data. Both results validate the superior performance in comparison with present filters in terms of accuracy and convergence, and the capacity to suppress the divergence problem caused by large prior error in magnetic dipole target tracking.

Key words: magnetic dipole, tracking, nonlinear filtering, linearization, Kalman filter

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