椭圆—笛卡尔变换结合LKF的多基地雷达目标跟踪算法

Target tracking using elliptic-Cartesian transformation and LKF in multi-static radar system

为了改善多基地雷达系统对高机动目标的跟踪性能,提出一种基于椭圆—笛卡尔变换结合线性卡尔曼滤波器(LKF)的目标跟踪算法。以最小化跟踪估计的均方差为目标,自适应选择发射器的发射波形和笛卡尔估计,通过椭圆—笛卡尔变换,将两个接收器所测量的时间延迟、多普勒平移和到达角度转换成笛卡尔坐标系中的目标位置和速度估计,使其与目标状态呈线性关系,并通过克拉美罗下界(CRLB)来表示笛卡尔估计的估计误差统计。最后,利用LKF进行滤波,进一步提高目标跟踪精度。实验结果表明,该算法在保持低计算量的同时,提供了较高的目标跟踪精度。

In order to improve the tracking performance of a multi-static radar system, this paper proposed a target tracking algorithm based on elliptic-Cartesian transformation and linear Kalman filter（LKF）. First, to minimize the mean square error of the tracking estimation as the goal, it adaptively selected the transmit waveforms and Cartesian estimates. Then, it used the elliptic-Cartesian transformation to transform the time delay, Doppler shift and angle of arrival into Cartesian coordinates of target position and velocity estimates, so that the estimate had linear relation with the target state. And it represented the estimation error statistics of Cartesian estimation through Cramer-Rao lower bounds （CRLB）. Finally, LKF was used to improve the target tracking accuracy. Experimental results show that the proposed algorithm provides a higher target tracking accuracy while keeping the low computation amount.