基于预测模型迭代优化的改进比例导引律

为了提高对加速度时变机动目标的制导精度,将预测决策理论与传统比例导引(PN)相结合,提出了一种通过泰勒级数预测模型迭代控制加速度修正项的改进比例导引律。首先,以预设的低阶泰勒级数预测模型预测特定时间的位移,并计算位移预测值与测量值的差值;然后,通过迭代方法逐阶增加泰勒级数预测模型阶数,直至满足精度要求;最后,计算泰勒级数预测模型的二阶导数,修正比例导引律的加速度指令。仿真结果表明,传统PN和APN的脱靶量分别约为195 m和95 m,提出的改进比例导引律的脱靶量约为8.3 m,极大地提高了制导精度。

Improved proportion navigation based on iterative optimization of prediction model

To improve the guidance precision to intercept the maneuvering target with time-varying acceleration, an improved proportion navigation with modified acceleration controlled iteratively by Taylor series prediction model is proposed by combining predictive decision theory with traditional proportion navigation(PN). Firstly, the displacement after certain time delay is predicted by using the preset low-order Taylor series prediction model, and the difference of the predicted displacement value and the measured value is calculated. Then, the order of Taylor series prediction model is increased step by step by iterative method until the accuracy requirement is met. Finally, the quadratic derivative of the Taylor series prediction model is calculated to modify the proportion navigation law. Simulation results show that the miss distances by traditional PN and APN are about 195 m and 95 m respectively, the miss distance by the improved proportion navigation proposed is about 8.3 m. That improves the guidance precision greatly.