摘要
对于单站的被动目标跟踪,在笛卡儿坐标系下建立跟踪模型,并用扩展的卡尔曼滤波(EKF)进行预测,得到的结果通常是不稳定且容易发散的。针对这种情况,提出了在修正的极坐标系下建立状态模型,摒弃传统的EKF算法,采用无迹卡尔曼滤波(UKF)算法,通过采样逼近非线性函数。数字仿真结果表明:在修正的极坐标中利用UKF算法得到的结果比EKF算法具有更快的收敛速度和更高的估计精度,且稳定性更好。
In single station passive tracking,when using Cartesian coordinates for establishing tracking models and the Extended Kalman Filter（EKF） for predicting,the results obtained are often unstable and easily get divergence.In view of this situation,we established the state model in the modified polar coordinates,and used Unscented Kalman Filter（UKF） in place of EKF.This algorithm approximates the nonlinear function through sampling.The simulation results proved that using the UKF algorithm in the modified polar coordinates can obtain a result with faster convergence speed,higher estimation accuracy and better stability.
出处
《电光与控制》
北大核心
2010年第11期34-38,共5页
Electronics Optics & Control
关键词
被动跟踪
纯方位
修正极坐标
无迹卡尔曼滤波
passive tracking
bearings-only
modified polar coordinates
Unscented Kalman Filter（UKF）
