Research On Radar Intelligent Anti-jamming Decision Method

With the development of the radar electronic intelligent countermeasures technology,radar needs to efficiently and accurately select the best anti-jamming method in order to counterwork the specific interference.Reinforcement learning(Reinforcement Learning,RL)has a strong decision-making ability,which can select the best anti-jamming measures accurately and quickly from the anti-jamming method library.Deep learning(Deep Learning,DL)can learn the characteristics of different interferences.Because it has a powerful learning ability.The combination of the two can make the radar more accurate,quickly and intelligently make anti-jamming decisions.Aiming at the problem that traditional radar systems cannot select corresponding anti-jamming methods for specific types of interference,this paper processes a radar anti-jamming intelligent decision-making method,which based on RL and deep reinforcement learning(Deep Reinforcement Learning,DRL)algorithm.The main research work and achievements are organized as follows:1.This paper introduces reinforcement learning theory and deep reinforcement learning theory.RL decision algorithm is used to generate the decision Q-value table.Based on the Q-value table,the intelligent decision is made for each jamming mode,and then the agent can get the best anti-jamming method.The traditional radar anti-jamming is separated from manual control,and the intelligent degree of radar anti-jamming is improved.2.The DRL algorithm is used to solve the problem that RL cannot handle a large amount of data.The sample data generated in the confrontation process is used to train the convolutional neural network,and we cam get the interference Q matrix,which is used as the basis for the radar to select the best anti-jamming method.Finally,we can get a welltrained anti-jamming intelligent decision-making module.The introduction of DRL algorithm can improve the ability of the system to deal with big data.By comparison,the intelligent decision-making method has the best decision-making performance,which is based on deep reinforcement learning.3.The core of this paper is the design and training of the anti-jamming intelligent decisionmaking module.The main process includes building a confrontation environment,data processing,and design decision-making methods.Compared with random selection strategies,intelligent decision-making strategy that is based on reincforcement learning algorithm and DRL algorithm has improved anti-interference accuracy and real-time performance,and the DRL method has the ability to process large data and high robustness,which makes it has great engineering value.