一种改进的海面舰船目标跟踪方法

摘要

When sea-surface target is tracked with surveillance radar, target moving range between two radar measures is comparable to the measurement error of radar because of its low velocity.In this case, the traditional Kalman filter pays a large amount of calculation while does not significantly improve tracking accuracy.To resolve this problem, a new tracking method called Projection Filter based on Linear Recursive Regression (PFLRR) for linear constant motion sea-surface target is presented.Simulation results show compared with Kalman filter, PFLRR gets the same filtering performance,while pays only two-fifths calculation.%用警戒雷达跟踪海面舰船目标时,由于舰船运动速度较慢,这使得其在雷达两次观测之间的位移与雷达本身的测量误差是可比拟的.这种情况下,传统的Kalman滤波在付出较大计算量的同时并不能显著提高跟踪精度.为此,针对海面匀速直线运动的舰船目标,提出了一种基于线性递推回归的投影滤波跟踪算法(Projection Filter based on Linear Recursive Regression,PFLRR),仿真实验表明该算法的滤波性能与Kalman滤波相当,但其计算量仅为Kalman滤波的2/5左右.