Path planning of intelligent radar anti-jamming matrix based on Q-learning algorithm

Intelligent radar is an important topic in the field of cognitive electronic warfare, and the application of reinforcement learning in intelligent radar anti-jamming decision-making has been a recent research focus. This paper proposes an antijamming matrix model composed of multiple radar configurations and anti-jamming patterns, and analyzes the path planning problem in radar anti-jamming decision-making using the Q-Learning algorithm. Simulation experiments demonstrate that using reinforcement learning allows the model to plan paths based on the environment autonomously and select the optimal path to achieve the best anti-jamming state ultimately.