A Graph-Aided Hierarchical Decision Framework for UAV Swarm Interception Under Saturation Incursions

The interception of saturation incursions by Unmanned Aerial Vehicle (UAV) swarms presents critical challenges in multi-agent coordination, including the curse of dimensionality, heterogeneous interaction effects, and multi-scale decision-making requirements. This paper proposes a Hierarchical Multi-scale Mean-Field DDPG (HM-MF-DDPG) framework augmented by graph sampling and aggregation networks to address these challenges. The framework introduces three key innovations: (1) a graph-enhanced weighted mean-field approximation that employs attention mechanisms to dynamically assess the contextual importance of neighboring agents, overcoming the homogeneity limitation of conventional mean-field methods; (2) a hierarchical decision architecture that separates strategic coordination (via graph attention networks) from low-level flight control (via improved gated recurrent units with situational awareness modulation); and (3) a distributed target assignment mechanism formulated as a potential game and solved via parallel auction algorithms, enabling collision-free allocation without central coordination. Extensive simulations in a constructed UAV swarm interception environment demonstrate that the proposed framework achieves a 93% interception success rate with 50 interceptors against 25 intruders, outperforming Deep Deterministic Policy Gradient (DDPG) and Mean-Field DDPG (MF-DDPG) baselines in both convergence speed and task efficiency. The framework exhibits robust generalization across varying No-Fly Zone (NFZ) configurations and swarm scales, providing a scalable solution for cooperative interception under saturation incursions.