目标跟踪中两种非线性滤波算法的对比研究  

Comparative Research on Two Nonlinear Filtering Algorithms in Target Tracking

作　　者：彭云辉[1] 刘云峰[1] 杨小冈[1] 缪栋[1] 

机构地区：[1]第二炮兵工程学院,西安710025

出　　处：《战术导弹技术》2008年第3期57-61,共5页Tactical Missile Technology

摘　　要：在处理目标跟踪等动态系统实时估计问题中,通常采用EKF作为状态估计方法提高估计精度.由于EKF进行非线性估计存在一些缺陷,将系统进行线性化近似存在估计误差,从而影响目标跟踪的精度.为了获得更高的估计精度,介绍了两种新的非线性滤波算法,即unscented卡尔曼滤波算法和粒子滤波算法.分析了UKF和PF算法的原理和算法实现,对两种算法的适应性进行了比较.通过目标跟踪仿真实验,表明粒子滤波算法估计精度比UKF算法高,但是计算量却相对较大.In dealing with real-time estimation of dynamic system,such as target tracking,the extended Kalman filter(EKF) is used as a state estimation method to improve the estimation accuracy.However,there is estimation error in linearizing system due to the defects of EKF in nonlinear estimation,which affects the accuracy of target tracking.Two new nonlinear filter algorithms are presented in order to yield higher estimation accuracy.Two methods are unscented Kalman filter(UKF) and particle filter(PF).UKF algorithm...

关 键 词：粒子滤波 贝叶斯估计 非线性滤波 UNSCENTED卡尔曼滤波 

分 类 号：TJ765[兵器科学与技术—武器系统与运用工程]