摘要
传统算法在解决目标被动跟踪时存在有偏、收敛速度慢或发散等不足,文中将无迹卡尔曼滤波(UKF)算法应用到目标的被动跟踪.该算法是一种以扩展卡尔曼滤波算法为基本框架,以贝叶斯理论和UT变换为理论基础的新型滤波算法.根据UT变换的基本原理给出了滤波过程的具体计算步骤并进行了仿真计算.理论分析和仿真结果表明,UKF算法的性能相当于二阶高斯滤波器,UKF算法在目标被动跟踪中的滤波精度、稳定性和收敛时间都优于EKF算法.
The traditional algorithms applied in passive tracking have some shortages or disadvantages such as bias,slow convergence or divergence.To solve the problem,UKF algorithm is applied in passive tracking.This algorithm has its theory basis consisted of Bayesian theory and UT transform,and implements according to the frame of EKF.Theoretical analysis and simulation result indicate that the UKF has the same performance to 2-rank Gauss filter and has better performance than EKF algorithms in precision,stability and convergence time when it is applied to bearing-only target tracking.
出处
《武汉理工大学学报（交通科学与工程版）》
2009年第4期734-736,752,共4页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
国防重点实验室基金项目资助(批准号:51444060101JB1108)
关键词
纯方位
非线性滤波
扩展卡尔曼滤波
无迹卡尔曼滤波
bearings-only
nonlinear filtering
extended Kalman filter
unscented Kalman filter
