The performance of target tracking filter based on EKF depends on the initial state vector, in order to accelerate the convergence of the filter,a initialization algorithm was proposed. First,the state and measurement equations in modified polar coordinates were demonstrated, and extended kalman filter (EKF) was introduced to analyze the target motion. Then, a initialization algorithm of filter state vector based on nonlinear least squares estimation was addressed. A combined filtering structure was discussed for the application. The simulation results show that the filter mentioned in this paper was faster convergence but as precise as pure EKF.%基于EKF的纯方位目标状态滤波器的性能依赖状态初值的选取,为了有效地提高估计的收敛速度,提出了一种滤波器状态初始化方法.首先,简要阐述了修正极坐标系下的推广卡尔曼滤波算法(EKF).然后,基于非线性最小二乘法的思想,推导了一种滤波器状态初始化方法.针对实际应用背景,提出一种组合滤波器结构并进行了仿真验证.结果表明,该算法收敛速度快,滤波精度与EKF相当.
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