Digital Array Radar Intelligent Anti-Jamming Modeling And Simulation

With the development of radar electronic jamming and anti-jamming technology,the demand of radar for jamming environment perception and intelligent anti-jamming is growing.In recent years,the rapid development of reinforcement learning and the antijamming advantages of digital array radar have gradually become prominent,and the realization of intelligent anti-jamming of digital array radar has more research significance.Firstly,aiming at the problem that the traditional array radar cannot automatically identify the types of jamming,this thesis has completed the modeling and simulation of a variety of active jamming signals.Various jamming signals are processed in multiple feature domains,and the corresponding features are extracted.The extracted features are generated matrix to train the support vector machine(SVM)classifier,which can identify the jamming types of the enemy by training a complete jamming identification module.Then,in view of the fact that the types of radar known jamming cannot automatically select anti-jamming measures,and the manual operation-based anti-jamming methods are difficult to meet the requirements of complex and fast-changing jamming scenarios,this thesis proposes to use the digital array radar as a platform to complete the simulation of three anti-jamming measures,namely,side lobe cancellation(SLC),side lobe shadowing(SLB)and anti-active hybrid jamming,by taking advantage of the unique advantages of digital array radar in anti-jamming,and apply the Q learning algorithm in reinforcement learning to the anti-jamming module to complete the modeling and simulation of intelligent anti-jamming of digital array radar.At the same time,the jamming identification module and the anti-jamming module are combined to generate active jamming signals and identify them.Corresponding anti-jamming measures or optimal anti-jamming strategies are adopted for the identified jamming signals.Finally,the jamming identification module and the anti-jamming module are verified on the interface of the simulation research platform,so as to realize the closed-loop processing of the radar simulation system from transmitting radar signals to the environment and then to receiving.The focus of this thesis is the design and simulation of jamming identification and antijamming module.The main process includes the feature extraction of jamming signal,the training of classifier,the simulation of anti-jamming measures,the simulation of antijamming performance index,and the design and training of reinforcement learning model.Applying reinforcement learning to the anti-jamming process can intelligently select the optimal anti-jamming strategy.The follow-up research of intelligent anti-jamming module is of great value.By combining machine learning and other technologies,the potential of anti-jamming of digital array radar is excavated,so as to further improve the anti-jamming ability of digital array radar and make the realization of intelligent anti-jamming system have greater application value.