Research On Cluster Cooperative Jamming Technology

With the development of radar network technology,it is difficult for target to evade the detection of radar network during penetration.The traditional single jammer has difficulty in making a substantial impact when jamming radar network,which cannot effectively protect the penetration target.In order to effectively protect the penetration target.This thesis mainly studies the cluster cooperative interference technology.The main contents of this thesis include:Firstly,the jamming principle of the cluster jamming pattern and the jammer deployment method are studied.The principles of suppression jamming and deception jamming are sorted out,and simulation analysis is carried out on these two types of jamming patterns.For the arrangement method of cluster jammers,this thesis studies the changes in the detection area before and after jamming of single radar and radar network,and analyzes the optimal use of jamming resources when the number of adjacent jammers is fixed,and the effectiveness of cluster jamming has been demonstrated through simulation studies.Secondly,the detection probability,localization and tracking accuracy of the radar network are used as interference evaluation indicators.According to the detection probability,localization and tracking accuracy of the radar network,this thesis establishes two cluster cooperative interference effect evaluation models: index weight and vector.The evaluation index vector model is used to solve the problem that the traditional weighting model cannot determine the size of the weight.The construction process of the two cluster cooperative interference effect evaluation models is as follows: firstly,the interference effect assessment criteria is determined in five areas: time domain,air domain,frequency domain,processing domain,and interference pattern;then the interference assessment index value under the specific surprise scenario is calculated according to the interference assessment criterion;and finally the weighted model of assessment index and vector model of assessment index are obtained by the weighting way and vectorization way.Through the simulation of three interference evaluation indexes and the overall evaluation of interference effect,the reasonableness and validity of the interference assessment index and model are verified.Finally,the particle swarm optimization algorithm,binary particle swarm optimization algorithm and genetic algorithm are improved,and the fast non-dominated multi-objective genetic algorithm(Non-dominated Sorting Genetic Algorithm II,NSGA-II)is introduced into the field of interference resource allocation to realize the cluster coordination.The process of jamming resource allocation is as follows: firstly,the threat level is evaluated according to the different working states of the enemy radar;then,the interference resource allocation model is established according to the jamming object and jamming pattern;finally,according to the jamming resource allocation model and the jamming effect,the evaluation model uses different intelligent algorithms to allocate interference resources.Compared to traditional algorithms,the three improved algorithms have better convergence speed in solving the problem of interference resource allocation by simulation,and the effectiveness of the NSGA-II algorithm for interference resource allocation is also proved.In this thesis,the time domain,air domain,frequency domain,processing domain and interference patterns are simultaneously incorporated into the interference assessment criteria,and an evaluation index vector model is proposed to improve the accuracy of interference assessment results.Finally,the NSGA-II algorithm is introduced into the field of interference resource allocation,and the effectiveness of the NSGA-II algorithm for interference resource allocation is demonstrated by simulation.