Dynamic Jamming Policy Generation for Netted Radars Using Hybrid Policy Network

Radar jamming resource allocation is crucial for maximizing jamming effectiveness and ensuring operational superiority in complex electromagnetic environments. However, the existing approaches still sufferfrom inefficiency, instability, and suboptimal global solutions. To address these issues, this work proposes addressing effective jamming resource allocation in dynamic radar countermeasures with multiple jamming types. A deep reinforcement learning framework is designed to jointly optimize transceiver strategies for adaptive jamming under state-switching scenarios. In this framework, a hybrid policy network is proposed to coordinate beam selection and power allocation, while a dynamic fusion metric is integrated to evaluate jamming effectiveness. Then the non-convex optimization is resolved via a proximal policy optimization version 2 (PPO2)-driven iterative algorithm. Experiments demonstrate that the proposed method achieves superior convergence speed and reduced power consumption compared to baseline methods, ensuring robust jamming performance against eavesdroppers under stringent resource constraints.