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

Shasha Shi; Qingsong Zhou; Jialong Qian; Shujie Shi

doi:10.1117/12.3029148

6 May 2024 Path planning of intelligent radar anti-jamming matrix based on Q-learning algorithm

Shasha Shi, Qingsong Zhou, Jialong Qian, Shujie Shi

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131072L (2024) https://doi.org/10.1117/12.3029148
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shasha Shi, Qingsong Zhou, Jialong Qian, and Shujie Shi "Path planning of intelligent radar anti-jamming matrix based on Q-learning algorithm", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131072L (6 May 2024); https://doi.org/10.1117/12.3029148

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