Radar Anti-Jamming Decision-Making Method Based on DDPG-MADDPG Algorithm

Abstract

In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the optimal anti-jamming effect. Therefore, an intelligent radar anti-jamming decision-making method based on the deep deterministic policy gradient (DDPG) and the multi-agent deep deterministic policy gradient (MADDPG) (DDPG-MADDPG) algorithm is proposed. Firstly, by establishing a typical working scenario of radar and jamming, we designed the intelligent radar anti-jamming decision-making model, and the anti-jamming decision-making process was formulated. Then, aiming at different jamming modes, we designed the anti-jamming improvement factor and the correlation matrix of jamming and ECCM. They were used to evaluate the jamming suppression performance of ECCMs and to provide feedback for the decision-making algorithm. The decision-making constraints and four different decision-making objectives were designed to verify the performance of the decision-making algorithm. Finally, we designed a DDPG-MADDPG algorithm to generate the anti-jamming strategy. The simulation results showed that the proposed method has excellent robustness and generalization performance. At the same time, it has a shorter convergence time and higher anti-jamming decision making accuracy.

AK-MADDPG-Based Antijamming Strategy Design Method for Frequency Agile Radar

“…Ref. [ 21 ] employed the DDPG-MADDPG (Deep Deterministic Policy Gradient and the Multi-Agent Deep Deterministic Policy Gradient) method for adaptive selection of multiple domain antijamming measures, including composite jamming types, using the antijamming improvement factor as feedback. Refs.…”

“…Refs. [ 19 , 20 , 21 ] all require online recognition of jamming and acquisition of jamming state information to intelligently select antijamming measures. However, the complexity and diversity of jamming signals make the design of online recognition algorithms another challenge.…”