基于方根分解形式的UKF算法在目标跟踪中的应用  

Applications of Unscented Kalman Filter Based on Square Root Decomposition to Target Tracking

作　　者：彭云辉[1] 杨小冈[1] 曹菲[1] 缪栋[1] 

机构地区：[1]第二炮兵工程学院303教研室,陕西西安710025

出　　处：《现代防御技术》2007年第5期120-123,129,共5页Modern Defence Technology

摘　　要：UKF作为一种新的非线性滤波方法已在目标跟踪问题中得到应用,在状态的时间更新阶段直接使用非线性模型,不引入线性化误差,而且不必计算Jacobians矩阵,相对于扩展卡尔曼滤波(EKF)不仅能提高滤波精度,而且更容易实现。提出了一种基于方根分解形式的UKF算法(SRD-UKF),算法的方根形式增加了数字稳定性和状态协方差的半正定性。通过BOT(bearing of target)仿真实验结果表明,该算法与UKF和PF算法相比具有更好的滤波性能。　As a new nonlinear filtering method,UKF（unscented Kalman filter） is applied in target tracking,which directly uses nonlinear models in the time-update phase.As a result,no error caused by linearization is introduced.Moreover,it is unnecessary to calculate the Jacobians matrix.Compared with the EKF（extended Kalman filter）,the UKF not only can improve the precision of filtering,but also can be realized easily.The theory and algorithm of the unscented Kalman filter based on square root decomposition（SRD-UKF） are introduced in this paper,and the square root forms have the added benefit of numerical stability and guaranteed positive semi-definiteness of the state covariances.By a BOT experiment,the simulation results show that the estimation performance of the SRD-UKF is better than UKF and PF.

关 键 词：状态估计 UNSCENTED卡尔曼滤波 UT变换 非线性滤波 

分 类 号：TN911.7[电子电信—通信与信息系统] TP391.9[电子电信—信息与通信工程]