Radar-Jamming Decision-Making Based on Improved Q-Learning and FPGA Hardware Implementation

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 7; p. 1190
• Main Authors: Zheng, Shujian, Zhang, Chudi, Hu, Jun, Xu, Shiyou
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Algorithms; Central processing units; Cognitive ability; Comparative analysis; Countermeasures; Decision making; Deep learning; Digital integrated circuits; Electronic warfare; Field Programmable Gate Array; Field programmable gate arrays; Game theory; Hardware description languages; Jamming; Learning; Machine learning; Methods; Microprocessors; Radar; Radar systems; radar-jamming decision-making; Reinforcement; reinforcement learning; Software; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16071190

In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation.