基于时空信息融合的多机协同空战决策方法OA

Multi-agent reinforcement learning is currently one of the most promising methods for achieving autono-mous cooperative air combat among multiple aircraft.However,existing methods are constrained by the end-to-end network architecture,facing critical issues such as poor multi-UAV coordination and difficulty in reflecting decision-making motivation in air combat.To address these issues,this paper proposes a multi-UAV cooperative air combat decision-making method based on spatial-temporal information fusion to improve the cooperation and interpretability of multi-aircraft air combat.First,a spatial information fusion method based on graph attention mechanism is designed to aggregate local observations of agents and form global situation assessment,which enhances the information fusion efficiency and training efficiency of the fully connected evaluation network.Second,a spatial-temporal information fu-sion method combining cross-attention and gated recurrent unit is developed to aggregate spatial and temporal infor-mation of enemy and friendly units,fusing coordination features for the policy network.Finally,a spatial-temporal in-formation fusion-based multi-UAV cooperative air combat decision-making algorithm is constructed by integrating rein-forcement learning and validated in a high-fidelity air combat environment.Experimental results show that the pro-posed method exhibits strong coordination and interpretability of decision-making motivation.