地磁背景下基于传感器阵列的磁偶极子目标跟踪方法

针对地磁背景下磁偶极子目标跟踪过程中存在的地磁干扰与模型非线性的问题，该文提出一种基于差量磁异常的蒙特卡洛卡尔曼滤波(MCKF)跟踪方法。新的跟踪方法以传感器阵列测量磁场的差量作为观测信号，并利用蒙特卡洛卡尔曼滤波算法解决模型的非线性问题，实现磁偶极子目标的实时跟踪。通过仿真跟踪实验，结果表明该文算法较传统的扩展或无迹卡尔曼滤波算法在稳定跟踪过程中对目标特征参数的估计更精确；通过地磁背景跟踪实验，结果验证了该文算法较传统算法在低信噪比下的性能优势。

Magnetic Dipole Object Tracking Algorithm Based on Magnetometer Array in Geomagnetic Background

In order to solve the problem of geomagnetic interference and model nonlinearity in the tracking process of magnetic dipole under geomagnetic background, Monte Carlo Kalman Filter (MCKF) tracking method based on differential magnetic anomaly is proposed in this paper. The new tracking method takes the difference of magnetic field measured by sensor array as the observation signal, and uses Monte Carlo Kalman Filtering (MCKF) algorithm to solve the nonlinear problem of the model to realize the real-time tracking of magnetic dipole targets. The simulation results show that the proposed method is more accurate than the traditional Extended Kalman Filter (EKF) or Untracked Kalman Filter (UKF) in the stable tracking process. The results of real geomagnetic background tracking experiments show that the proposed algorithm has better tracking performance under low SNR.