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。
This paper introduces a method for radar target tracking based on Sequential Unscented Kalman Fiher（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 Fiher（EKF）.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第25期202-204,217,共4页
Computer Engineering and Applications
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
Unscented Kalman Filter（UKF）
Extended Kalman Filter（EKF）
sequential filter
state estimation
radar target tracking
