Deep Q-Network for Cognitive Radar Anti-Jamming Strategy in Complex Electromagnetic Environment

The contemporary landscape of sophisticated and intelligent jamming techniques presents significant challenges for traditional radar anti-jamming methods, necessitating advancements in radar operational capabilities. To address the intricate and dynamic nature of jamming scenarios, alongside the limitations in performance assurance of manually crafted anti-jamming strategies and the suboptimal real-time responsiveness of radar systems, this study introduces an intelligent decision-making model founded on deep reinforcement learning (DRL). This model is meticulously structured, comprising a defined action space, state space, and reward function. Concurrently, the paper advocates a novel radar anti-jamming strategy learning approach based on the Deep Q-Network (DQN), adept at mitigating external malicious interference. This approach enhances the integration efficiency and the doppler frequency resolution in radar echo processing. Comparative simulation outcomes affirm the superiority of the proposed intelligent decision model and training methodology over established methods like Proximal Policy Optimization (PPO) and Q-Learning. Notably, the model demonstrates enhanced jamming suppression, robust generalization capabilities, accelerated response times, and a significant augmentation in the radar’s autonomous decision-making prowess.