As joint operations have become a key trend in modern military development, unmanned aerial vehicles (UAVs) play an increasingly important role in enhancing the intelligence and responsiveness of combat systems. However, the heterogeneity of aircraft, partial observability, and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods. To address these issues, this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning. First, a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments. Then, to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations, a sector-based partial observation mechanism is designed. A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions, including time to closest approach, minimum separation distance, and aircraft type. Furthermore, a Hierarchical Prioritized Experience Replay (HPER) mechanism is introduced, which classifies experience samples into high, medium, and low priority levels to preferentially sample critical experiences, thereby improving learning efficiency and accelerating policy convergence. Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed, environmental adaptability, and robustness, significantly enhancing collision avoidance performance and convergence rate. Finally, transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex, real-world joint operational scenarios.
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