Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles (UAVs) is one of the main combat modes in future air warfare, which becomes even more complicated with highly changeable situation and uncertain information of the opponents. As such, this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat. Firstly, a cooperative situation assessment model is presented to measure the overall combat situation. Secondly, an incomplete information dynamic game model is proposed to model the dynamic process of air combat, and a dynamic Bayesian network is designed to infer the tactical intention of the opponent. Then a reinforcement learning framework based on multi-agent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model. Finally, a series of simulations are conducted to verify the effectiveness of the proposed method, and the simulation results show effective synergies and cooperative tactics.