Radar Waveform Design Based on Multi-Agent Reinforcement Learning

Abstract

Under the actual combat background, prior information on radar targets has great uncertainty. The waveform designed based on prior information does not meet the requirements for the estimation of parameter. Thus, an algorithm for designing a waveform based on reinforcement learning is proposed to solve the above-mentioned problem. The problem on radar target parameter estimation is modeled as a framework for multi-agent reinforcement learning. Each frequency band acts as an agent, collectively interacts with the environment, independently receives observation results, shares rewards, and constantly updates the Q-network. The results of the simulation experiments indicate that the algorithm exhibits a significant improvement in terms of the mutual information obtained using the water injection method. In the case of simulation experiment, the SINR of the waveform designed based on multi-agent reinforcement learning is more than 3[Formula: see text]dB higher than that of LFM waveform. Under the condition of different time width and power, the mutual information obtained by the algorithm is better than that of water injection method. Moreover, such algorithm is also found to effectively improve the parameter estimation performance of target detection.