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

Abstract

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.

Keywords

• frequency diversity array (FDA)
• multi-agent reinforcement learning (MARL)
• multi-agent deep deterministic policy gradient (MADDPG)
• power allocation
• electronic warfare