高超声速巡航导弹的神经网络虚拟目标微分对策中制导律

针对高超声速巡航导弹的高空巡航飞行段，采用虚拟目标的定义方法，结合微分几何知识以及运动学方程建立了导弹与目标的相对运动模型，并在此基础上视其与虚拟目标之间的运动为单目标微分对策问题。利用哈密尔顿函数求解方法推导了开环微分对策中制导律，同时建立了一种新的闭环微分对策中制导律结构图，并对反向传播神经网络的训练样本进行了设计。通过反向传播神经网络的函数逼近功能实现了高超声速巡航导弹微分对策中制导律的智能化。仿真验证表明了其有效性。

VIRTUAL TARGET DIFFERENTIAL GAME MIDCOURSE GUIDANCE LAW FOR HYPERSONIC CRUISE MISSILE BASED ON NEURAL NETWORK

For the high altitude cruising flight phase of a hypersonic cruise missile (HCM),a relative motion mod-el between the missile and the target is established by defining virtual target and combining the theory of the dif-ferential geometry with missile motion equations.Based on the model,the motion between the missile and the tar-get is considered as a single target differential game problem,and a new open-loop differential game midcourseguidance law (DGMGL) is deduced by solving the corresponding Hamiltonian Function.Meanwhile,a new struc-ture of a closed-loop DGMGL is presented and the training data for back propagation neural network (BPNN) aredesigned.By combining the theory of BPNN with the open-loop DGMGL obtained above,the law intelligence isrealized.Finally,simulation is carried out and the validity of the law is testified.