用于卫星制导弹药落点预测的卡尔曼滤波算法  

Kalman Filtering Algorithm for Impact Point Prediction of Satellite-guided Projectile

作　　者：戴明祥[1] 杨新民[1] 易文俊[1] 何颖[1] 

机构地区：[1]南京理工大学瞬态物理国家重点实验室,南京210094

出　　处：《弹箭与制导学报》2013年第4期91-93,126,共4页Journal of Projectiles,Rockets,Missiles and Guidance

基　　金：总装预研基金资助

摘　　要：为即时地准确地预测弹丸落点,文中建立了基于质心弹道模型的卡尔曼滤波状态方程,利用衰减记忆法扩展卡尔曼滤波递推算法估计方程中的未知参数和风偏因素,并利用其估计参数外推出外弹道落点。利用某型号迫弹卫星虚拟导引装置的测量数据进行验算,其未知系数迅速收敛至真值,同时实现了对风偏的准确拟合。研究发现其卡尔曼滤波算法对弹丸落点预测具有精度高、运算速度快等优点,适宜于工程应用。In order to predict the impact point precisely and instantly, the state equation of Kalman filtering, based on the centroid ballistic model, has been built in the paper. Making using of memory attenuated extended Kalman filtering, unknown parameters and wind deflection in the state equation are estimated, which are used to extrapolate the impact point. As the result of checking with the real data of satellite seeker, the drag acceleration coefficient could be converged rapidly and the influence of wind deflection could also be simulated. What＇ s more, the kalman filtering algorithm for impact point prediction has less computational cost and is more accurate and simpler to apply for engineering.

关 键 词：落点预测 卫星制导 衰减记忆法 扩展卡尔曼滤波 

分 类 号：TJ765.3[兵器科学与技术—武器系统与运用工程]