Abstract: For the problem of real-time receding horizon pursuit and evasion games of missile guidance, this study sets up several groups of initial states of  confrontation missiles. Then the decomposing orthogonal collocation method is utilized to solve the bilateral open-loop optimal control of missiles pursuit and evasion games offline, which forms the training data set. The state and control variables of confrontation missiles at the initial and terminal moments are utilized as input and output respectively to train the neural network with the back propagation (BP) algorithm. In the simple, complex and uncertain environment, the BP neural network is utilized to estimate the bilateral open-loop optimal control of missiles in the short optimization cycle based on the receding horizon optimization framework. The states of confrontation missiles are fed back and updated, and the bilateral closed-loop optimal control of missiles pursuit and evasion games is solved in real time. This study compares the optimization results obtained by neural network and direct methods, respectively. The maximum errors of the capture point position and game time are 0.554% and 0.097%, respectively, which show that the optimization results of the two methods agree well. The calculation time of the neural network method is obviously reduced compared with the direct method, which improves the realtime solution of receding horizon pursuit and evasion games of missile guidance.

Key words: neural network, pursuit evasion games, missile guidance, receding horizon optimization