Abstract: In modern warfare, the multifunctional trend of radars, even multiple radars detecting targets together, enhances the anti-jamming capability of radars. However, the traditional jamming system still follows a fixed jamming strategy, and the real-time performance of decision-making facing large numbers of radars is poor. And the cognitive jamming study is urgent. The concept of reinforcement learning is explained and the difference between Q learning algorithm and double Q learning algorithm is compared. The reinforcement learning algorithm is used to establish a model based on cognitive electronic warfare to realize the allocation of radar jamming strategies. The simulation of the decision-making method shows that the two reinforcement learning algorithms can accomplish the task of jamming strategy allocation, and the double-Q learning algorithm works better in a multi-radar environment. It shows that the reinforcement learning algorithm can perform autonomous learning and complete the cognitive decision-making for the allocation of interference resources.

Key words: multifunctional radar, adaptive interference, double Q-learning, jamming decision-making