LAUSR

In the past decade, how to improve the fidelity of maneuver gaming and the synergy of target allocation has become a key issue in cooperative autonomous air combat researches. To address the problem, this study proposes a maneuver decision-making algorithm based on an optimized dynamic Bayesian network and a target allocation decision-making algorithm based on an optimized hybrid particle swarm optimization. In maneuvering decision-making, the state transition’s reliability and the air combat’s autonomy are enhanced through considering the effect of sliding mode control. The Bayesian network is improved through introducing a strategy for dynamic prior probability updating. The computation is reduced and the efficiency is increased through pruning the minimax search tree according to visual prediction. In target allocation decision-making, the algorithm’s convergence speed is greatly accelerated and the solution’s global optimality is improved through introducing immigrant particles. The algorithm’s application scope is expanded through proposing a solution principle about unequal quantity combat situations. Furthermore, the end criterion is specially designed to fit real-world combats through introducing a fire control. The simulation results show that the designed decision-making algorithms are more effective in solving the problem of cooperative autonomous air combat, which indicates that the various improvements introduced in this study are reasonable and effective.