Intelligent virtual opponent decision making and guidance method in short-range air combat training

To train pilots' short-range air combat skills, the traditional way based on flight simulation technology is to have multiple pilots operate multiple fighter simulators at the same time. If an intelligent virtual opponent is used to assist pilots in confrontation training, not only could the normal training process without other pilots be guaranteed, but the training cost could also be reduced to a great extent. In this paper, an integrated method based on dynamic Bayesian network (DBN) and constrained gradient algorithm is proposed to realize autonomous decision making and space occupancy guidance for intelligent virtual opponents in the attack and defense game during short-range air combat training. A dynamic Bayesian network model for short-range air combat decision making is established in combination with the space occupying situation, the fire control attack area and the identification results of maneuvering actions. This model realizes an intelligent selection of occupancy guidance index in accordance with the dynamic battlefield environment. A target trajectory prediction model is built for each type of maneuvers identified online to obtain the real-time prediction of the target trajectory. With the occupancy guidance index, target trajectory predication, and the flight performance constraints in consideration, a constraint gradient method is used to calculate the optimal occupancy guidance quantity of the intelligent virtual opponent. Thus, a seamless combination of space occupancy decision and guidance quantity calculation for intelligent virtual opponent is achieved. The simulation results of short-range air combat show that the proposed method can realize rational autonomous decision making and space occupancy guidance for intelligent virtual opponent, overcome the problem of solidifying the maneuver mode in traditional methods, and thus have better real time and optimization performance. © 2022, Editorial Board of JBUAA. All right reserved.