基于自适应新息卡尔曼滤波的脱靶量预测算法  

Algorithm of Miss Distance Prediction Based on Innovation Adaptive Kalman Filter

作　　者：杨丽君 刘博 王军[1] 陈天群 雷俊杰 YANG Li-jun;LIU Bo;WANG Jun;CHEN Tian-qun;LEI Jun-jie(School of Automation, Nanjing University of Science and Technology, Nanjing 210094;Shanghai Electro-Mechanic Engineering Institute, Shanghai 201109, China)

机构地区：[1]南京理工大学自动化学院,江苏南京210094 [2]上海机电研究所,上海201109

出　　处：《指挥控制与仿真》2021年第6期46-52,共7页Command Control & Simulation

基　　金：国家自然科学基金(61104197)。

摘　　要：为提高弹丸脱靶量预测的精度,建立了基于自适应新息卡尔曼滤波算法的弹丸脱靶量递推预测模型。首先,利用各误差源特性建立误差源模型,结合其特性叠加生成合理的脱靶量序列,并建立自适应新息卡尔曼滤波的状态方程。然后,利用新息序列计算系统噪声矩阵和量测噪声协方差矩阵,构建自适应新息卡尔曼滤波预测模型。最后,将改进后的预测结果与传统卡尔曼滤波预测对比分析。Matlab仿真结果表明:卡尔曼滤波的性能和估计准确性依赖于系统模型和噪声统计特性,改进后的新息卡尔曼滤波模型的预测结果更加精确。同时,在随机脱靶量序列中,该算法具有普适性。In order to solve the problem of improving the accuracy of projectile miss-distance prediction,a recursive prediction model of projectile miss-distance based on innovation adaptive Kalman Filter algorithm is established.Firstly,it uses the characteristics of each error source to establish an error source model,combines its characteristics to superimpose a reasonable miss distance sequence,and establishes innovation adaptive Kalman Filter state equation.Then,the innovation sequence is used to calculate the system noise matrix and the measured noise covariance matrix to construct innovation adaptive Kalman Filter prediction model.Finally,the improved prediction results are compared and analyzed with the traditional Kalman Filter prediction.Matlab simulation results show that the performance and estimation accuracy of Kalman Filter depend on the system model and noise statistical characteristics,and the improved Innovation Adaptive Kalman Filter model is more accurate in predicting results.At the same time,the algorithm has universal applicability in random miss distance sequence.

关 键 词：误差源分类 射击误差模型 新息卡尔曼滤波 递推预测 脱靶量预测 

分 类 号：TJ760.62[兵器科学与技术—武器系统与运用工程] TN713[电子电信—电路与系统]