A Context-Aware Feature Fusion Method for Multi-UAV Cooperative Air Combat

Multi-UAV autonomous cooperative air warfare is an important mode of future intelligent air warfare. However, due to the complexity and uncertainty of air combat situation information, how to accurately interpret the enemy’s sustained combat intent remains a major challenge. To address this problem, we propose a Context-Aware Adaptive Feature Fusion (CAAFF) method, which can effectively utilize the time-series data of battlefield situation for hierarchical feature fusion. Specifically, the input data is first subjected to dimensionality reduction processing and feature extraction by an encoder-decoder to provide high-quality low-dimensional feature representations for further feature fusion. Next, the middle layer captures attitude changes by aggregating information from neighboring nodes via a graph attention convolutional network (GACN), flexibly fusing the features of each node, and identifying complex relationships between nodes. Finally, the mechanism of stabilizing multi-attention with self-attention is used to integrate global information at the upper layer to construct an overall feature representation of the mission and realize local-to-global posture analysis. In order to enhance the interpretation of persistent operational intent, we utilize the context-aware module to construct contextual feature representations by combining current and historical state information, thus improving the depth and interpretability of mission understanding. Finally, we combine the CAAFF method with reinforcement learning and verify its performance through multiple experiments, demonstrating the applicability and effectiveness of the method in Multi-UAV cooperative air combat.