基于改进自适应卡尔曼滤波的闭环脱靶量预测技术研究

Research on the Prediction Technology of Closed-Loop Target-Missing Quantity Based on Improved Adaptive Kalman Filtering

针对传统卡尔曼滤波算法的脱靶量预测稳定性差、精度不高等问题,提出了一种基于改进自适应卡尔曼滤波的脱靶量预测方法。利用脱靶量误差源统计特性建立脱靶量模型,结合改进自适应卡尔曼滤波方法实现对脱靶量的准确估计,并将改进自适应卡尔曼滤波与传统卡尔曼滤波的预测修正结果对比分析。仿真结果表明:基于改进自适应卡尔曼滤波算法比传统卡尔曼滤波算法在闭环校射中方位角预测修正提高70%以上,高低角预测修正提高30%以上,该改进卡尔曼滤波算法预测结果更加稳定、精确。

Aiming at the problems of poor stability and low accuracy of target-missing quantity prediction based on traditional Kalman filtering algorithm,a target-missing quantity prediction method based on improved adaptive Kalman filtering was proposed.Firstly,the target-missing quantity model was established according to the statistical characteristics of miss error.Then the target-missing quantity was estimated accurately based on the improved adaptive Kalman filtering method.Finally,the prediction results of the improved adaptive Kalman filtering and the traditional Kalman filtering were compared and analyzed.The simulation results show that the prediction values of azimuth angle based on the improved adaptive Kalman filtering algorithm are more than 70%higher than that based on the traditional Kalman filtering algorithm,and prediction values of elevation angle increase by more than 30%.In addition,prediction results based on improved Kalman filtering algorithm are more stable and accurate.