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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。
Aiming at the problem of magnetic target tracking, a discrete state space model of magnetic target tracking is established based on the equivalent field source model of magnetic dipole, and the real-time tracking of magnetic dipole target is transformed into the problem of filtering estimation of state space model. Aiming at the problem that initial conditions of magnetic target are difficult to obtain and the existing Kalman filter algorithm is prone to divergence under large initial error conditions, a new Kalman filter algorithm for recursive observation is proposed. The existing one-step update is described as Hand Push the update process, equivalent to reduce the large initial error caused by a large nonlinear error. The simulation and measured data test results show that the proposed algorithm has good accuracy and convergence, and can effectively suppress the filter divergence caused by the large initial error in the magnetic dipole tracking, which is suitable for practical application.