Enhanced Radar Anti-Jamming With Multi-Agent Reinforcement Learning

As jamming systems continue to evolve, jammers are becoming increasingly complex. They modify their strategies in response to changes in radar status, thereby creating a dynamic confrontation with the radar. Currently, deep reinforcement learning (DRL) methods are widely employed in the decision-making process for radar anti-jamming strategies; however, most of these methods overlook the learning capabilities of the jammer. This letter proposes a novel multi-agent deep reinforcement learning (MADRL) method that leverages the robust capabilities of the neural fictitious self-play (NFSP) method and extensive-form game (EFG) frameworks to address multi-agent interactions and partial information observability challenges. The letter primarily discusses the model design, which includes the construction of the EFG framework, hierarchical optimization of the action space, and the design of the reward function. Comparative results with other state-of-the-art methods demonstrate that this approach achieves significant accuracy and rapid convergence in anti-jamming decision-making, thereby effectively enhancing the intelligent anti-jamming capabilities of radar systems.