摘要
大气层外动能拦截器以直接碰撞的方式对弹道目标进行拦截,需要对目标状态的精确估计,然而仅有视线角测量信息的传统状态估计方法在估计精度或收敛性方面有所欠缺。在视线坐标系下建立拦截器和目标的相对运动模型,利用迭代思想对无迹卡尔曼滤波(Unscented Kalman Filter,UKF)算法进行改进,设计出基于迭代无迹卡尔曼滤波(Iterated Unscented Kalman filter,IUKF)的状态估计器,并与采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)和UKF方法的估计器进行仿真比较。结果表明,IUKF算法不仅具有较高的估计精度,而且具有更好的收敛性,促进了拦截效果,提高了拦截精度。
Exo - atmospheric kill vehicles intercept ballistic missiles by means of hit - to - kill technology, which requires accurate state estimation. However, traditional estimate methods are weak in either precision or convergence because of the nonlinearity caused by angle only measurements. A relative motion model between interceptor and the target was built in line -of- sight（LOS） coordinate system. By using the idea of iteration to modify the Unscented Kalman Filter （UKF）, the Iterated Unscented Kalman Filter （IUKF） was proposed to deal with the nonlinearity. Compared with the methods of Extended Kalman Filter（EKF） and UKF in the simulation, the IUKF was proved to have higher estimation accuracy and perform better in convergence, which has a positive effect on the final intercep- tion.
出处
《计算机仿真》
CSCD
北大核心
2015年第8期32-36,共5页
Computer Simulation
关键词
大气层外动能拦截器
视线
非线性
状态估计器
Exo - atmospheric kill vehicle
Line - of - sight
Nonlinearity
State estimator
