基于神经网络剩余时间估计的导弹-目标动态分配方法  

Missile-target Dynamic Allocation Method Based on Neural Network Residual Time Estimation

作　　者：苏适 南英 何明勇 SU Shi;NAN Ying;HE Mingyong(College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,Jiangsu,China)

机构地区：[1]南京航空航天大学航天学院,江苏南京211106

出　　处：《上海航天（中英文）》2024年第5期145-152,共8页Aerospace Shanghai（Chinese&English）

基　　金：航空科学基金(201929052002)。

摘　　要：针对防空导弹动态目标分配问题,设计了一种基于神经网络的剩余飞行时间预测方法,该方法在计算中不仅考虑了弹目相对距离和导弹运动状态,还考虑了敌方目标的运动状态对预测结果的影响,提升了预测精度;结合该方法建立了综合考虑距离优势、角度优势和剩余飞行时间优势的目标分配模型,采用拍卖算法对该模型进行求解,给出整体最优的目标分配方案。仿真结果表明:神经网络模型的测试集预测误差在1 s以内,低于经典算法,拍卖算法的重分配计算用时能够满足系统的实时性要求。A neural network-based residual flight time prediction method is designed for the dynamic target allocation of anti-aircraft missiles.This method not only takes into account the relative missile-to-target distance and the missile motion state,but also considers the influence of the motion state of the enemy target on the prediction results,thereby improving the prediction accuracy.A target allocation model,which comprehensively considers the distance advantage,angle advantage,and residual flight time,is established in combination with the designed method.An auction algorithm is adopted to solve this model,by which an overall optimal target allocation scheme is achieved.The simulation results show that the prediction error of the neural network model is within 0.4 s,which is lower than that of the classical method.The computation time for reallocation with the auction algorithm can meet the real-time requirements of the system.

关 键 词：防空导弹 目标分配 拍卖算法 剩余飞行时间 神经网络 

分 类 号：V448.232[航空宇航科学与技术—飞行器设计]