Reinforcement Learning for FDA-MIMO Radar Power Allocation in Congested Spectral Environments

Zhou, Changlin; Wang, Chunyang; Gong, Jian; Tan, Ming; Bao, Lei; Liu, Mingjie

doi:10.1016/j.eswa.2024.123957

Cited by 1 publication

(1 citation statement)

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“…For the parametric measurement model in which target response varies with the transmitting frequency of the FDA radar, the frequency domain power allocation between the FDA radar and the jammer should not be regarded as a set of discrete actions [40]. Power allocation should be a continuous control problem; the action space is a continuous set [41].…”

Section: Ddpg Algorithmmentioning

confidence: 99%

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

Zhou,

Wang,

Bao

et al. 2024

Remote Sensing

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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.

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Section: Ddpg Algorithmmentioning

confidence: 99%