UAV's air combat decision-making based on deep deterministic policy gradient and prediction

Abstract

To solve the enemy uncertain manipulation problem during a UAV's autonomous air combat maneuver decision-making, this paper proposes an autonomous air combat maneuver decision-making method that combines target maneuver command prediction with the deep deterministic policy algorithm. The situation data of both sides of air combat are effectively fused and processed, the UAV's six-degree-of-freedom model and maneuver library are built. In air combat, the target generates its corresponding maneuver library instructions through the deep Q network algorithm; at the same time, the UAV on our side gives the target maneuver prediction results through the probabilistic neural network. A deep deterministic policy gradient reinforcement learning method that considers both the situation information of two aircraft and the prediction results of enemy aircraft is proposed, so that the UAV can choose the appropriate maneuver decision according to the current air combat situation. The simulation results show that the method can effectively use the air combat situation information and target maneuver prediction information so that it can improve the effectiveness of the reinforcement learning method for UAV's autonomous air combat decision-making on the premise of ensuring convergence.