摘要
文章提出了一种基于UKF的弹道导弹跟踪算法—Singer-UKF算法。传统的弹道导弹跟踪算法，利用扩展卡尔曼滤波器进行非线性滤波，取Taylor展开的前两项，可能引入较大的线性化误差，导致跟踪精度不高。文章在分析弹道目标动力学特性的基础上对目标的运动模型采用基于J2校正的Singer模型来描述，并针对非线性的目标量测模型应用不敏卡尔曼滤波算法。将该算法与扩展卡尔曼滤波算法进行蒙特卡罗仿真比较，仿真结果表明该算法的跟踪效果更好。
A tracking algorithm named Singer-UKF to ballistic missile is presented in this paper. The conventional technique for tracking the ballistic missile is the extended Kalman fiher（EKF）, which relies on approximating the nonlinear models by the first two terms of its Taylor series expansion. However, EKF may introduce large linearization error and result in the low tracking accuracy. In this paper, the Singer model based on J2 Correction is adopted to describe the target motion by analyzing the ballistic missile＇ s kinematics feature, and the unsented Kalman filter is utilized to the nonlinear measurement models. The new algorithm was compared with the extended Kalman filter by Monte-Carlo simulation. The results show that the algorithm has better tracking performance.
出处
《电子对抗》
2008年第5期36-41,共6页
Electronic Warfare
