Frequency Diversity Array Radar and Jammer Intelligent Frequency Domain Power Countermeasures Based on Multi-Agent Reinforcement Learning

Autor: Changlin Zhou, Chunyang Wang, Lei Bao, Xianzhong Gao, Jian Gong, Ming Tan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Remote Sensing, Vol 16, Iss 12, p 2127 (2024)
Druh dokumentu: article
ISSN: 16122127
2072-4292
DOI: 10.3390/rs16122127
Popis: With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment.
Databáze: Directory of Open Access Journals
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