为了测量智能瞄具狙击步枪系统瞄准线偏差, 控制击发时机并提高射击精度, 提出了滤波加权融合方法与线性预测击发判据。运用卡尔曼预测滤波对脱靶量滞后进行补偿, 得出瞄准线偏差;将陀螺数据滤波后积分得出另一瞄准线偏差;根据加权融合算法, 得到加权融合后瞄准线偏差。建立了基于陀螺信号、融合信号和电动击发装置特性的线性预测击发判据;搭建了dSPACE实验测试系统, 进行了瞄准线偏差测量实验和模拟对比射击实验, 结果表明: 滤波加权融合后的信号基本可表示瞄准线实际偏差; 在滤波加权融合方法与线性预测击发判据条件下模拟射击时, 弹着点分布在0.05 mrad圆内的概率为85.7%, 高于简单控制击发模式下53.33%的概率。

In order to measure deviations of intelligent sniper system’s line of sight and improve firing accuracy by controlling firing time, filter weighted fusion method and linear predicting firing criterion are proposed in this paper.Kalman predicting filter is firstly used to compensate missing distance delay and line of sight deviation is obtained, meanwhile data filtered of gyroscope is integrated and another line of sight deviation is obtained. Secondly, weighted line of sight deviation based on weighted fusion algorithm is obtained. As a result, linear predicting firing criterion is built, which is based on data of gyroscope, weighted fusion algorithm and characteristic of electric firing device. Finally, dSPACE test system is built for experimental measurement of line of sight deviation and simulation of firing contrast experiment respectively. Results indicate that data of filter weighted fusion can basically present real line of sight deviation. When firing simulation is carried with filter weighted fusion method and linear predicting firing criterion, chances of impact point distributed in a circle with a diameter of 0.05 mrad is 85.7%, higher than chance of simple control firing mode which is 53.33%.