扩展卡尔曼滤波器(EKF)已被广泛用作一种非线性滤波方法来解决雷达的跟踪问题。但是,已经发现,在目标位置横向距离测量误差较大的情况下,由于不可忽略的非线性效应,一般EKF的性能会大大降低。基于下述事实,即在直角坐标系中可以正确估算测量误差协方差,开发了一种新的滤波方法以改善利用雷达测量值的跟踪性能。所提出的算法可看作是EKF的改进。在EKF中,距离测量误差的方差是以自适应方式估计的。这种滤波器结构方便了序列测 Extended Kalman Filter (EKF) has been widely used as a non-linear filtering method to solve the radar tracking problem. However, it has been found that in the case of large measurement errors in the lateral distance of the target location, the performance of the general EKF is greatly reduced due to non-negligible non-linear effects. Based on the fact that measurement error covariances can be correctly estimated in Cartesian coordinates, a new filtering approach has been developed to improve the tracking performance using radar measurements. The proposed algorithm can be seen as an improvement of EKF. In EKF, the variance of the distance measurement error is estimated adaptively. This filter structure facilitates sequencing