Abstract: This paper introduces a method for radar target tracking based on Sequential Unscented Kalman Filter（SUKF）.In UKF，a minimal set of carefully chosen sample points is used to represent random variables distribution.And when propagated through the true nonlinear system，these sample points capture the mean and covariance accurately to the 3rd order for nonlinear transformation.In order to improve filtering accuracy，SUKF is applied to a radar target tracking system.The Monte Carlo simulation demonstrates that the SUKF has higher filtering accuracy and computational efficiency than conventional Extended Kalman Filter（EKF）.

Key words: Unscented Kalman Filter（UKF）, Extended Kalman Filter（EKF）, sequential filter, state estimation, radar target tracking

摘要： 提出一种基于序贯无迹卡尔曼滤波的雷达目标跟踪方法。雷达跟踪系统为离散非线性系统，传统的解决方法是使用扩展卡尔曼滤波。无迹卡尔曼滤波用少量采样点表示随机变量的分布，通过非线性系统传播，能以三阶精度获得非线性变换的均值和方差的估计。为了提高无迹卡尔曼滤波的精度，用序贯无迹卡尔曼滤波方法依次处理方位角、俯仰角和距离，来进行雷达目标跟踪。通过Monte Carlo仿真，验证了该滤波算法比传统的扩展卡尔曼滤波具有更高的滤波精度和更高的计算效率。

关键词: 无迹卡尔曼滤波, 扩展卡尔曼滤波, 序贯滤波, 状态估计, 雷达目标跟踪