Research On Multi-vector Synthesis Interference Control Algorithm

The electromagnetic spectrum has become the sixth combat zone in modern warfare,and monopulse radar is widely used in electromagnetic warfare.The use of multivariate vector synthetic jamming has significant jamming effects on monopulse radar,which is of great military significance in modern warfare.In this thesis,the control algorithm in multivariate vector synthetic jamming technology is investigated.A mathematical model of jamming is established with the help of a swarm optimisation algorithm to quickly find the appropriate amplitude and phase centre parameters,so that a series of equivalent radiation centres obtained by synthesis under consideration of the effect of device errors meet the minimum jamming requirements.The multivariate vector synthesis interference technology algorithm is based on the near-field effect error correction.Based on its theoretical analysis of the model,the thesis considers comprehensively the actual experimental control accuracy and the problem of whether the amplitude and phase parameters are realizable.From the perspective of the actual interference experiments,determine the optimization goal,which is to find a reasonable amplitude and phase centre parameters,so that under the influence of device error synthesis the interference interference point position can still meet the minimum interference requirements.The thesis mainly uses group optimization algorithm to achieve numerical solution with qualified conditions.From the energy flow point of view,establish a mathematical model of the equivalent radiation centre of the ternary vector synthesis-Poynting vector theorem,study the law of interference synthesis,determine the input and output parameters of the control algorithm according to the existing ternary group experimental platform,then propose two optimisation schemes of the control algorithm.After comparing the advantages and disadvantages,the final optimization scheme is determined.The final optimization scheme is realized based on the two population optimization algorithms and the advantages and disadvantages are compared.The two population optimisation algorithms,one is Particle Swarm Optimization(PSO),and three improved algorithms are proposed: improved time-varying PSO based on logistic-mapping and time-varying parameters(LTPSO),adaptive PSO based on Adaptive Grouping Strategy(AGPSO),and Particle Swarm-Differential Evolution hybrid algorithm(PSO-DE).The improved results show a significant improvement in computational efficiency compared with Genetic Algorithm(GA)and DE,illustrating the effectiveness of the improved strategies.AGPSO is the most efficient algorithm between them.The other is Artificial Bee Colony algorithm(ABC).Two Improved strategies are proposed: Improved Artificial Bee Colony(IMABC)algorithm and ABC-DE hybrid algorithmThe algorithm was compared with the Genetic Algorithm(GA)and DE,showing the effectiveness of the improved strategy,ABC-DE hybrid algorithm,the two algorithms are more computationally efficient compared with the standard ABC algorithm,and ABCDE is better than IMABC in jumping out of the local optimum and succeeding in finding the optimum with fewer iterations.Finally,a comparison of the optimisation results between AGPSO and ABC-DE was carried out.The algorithm in this thesis is designed to provide fast data support for multivector synthetic jamming test experiments or for aircraft and naval platforms equipped with ternary jamming systems to find the parameters that meet the minimum jamming requirements in a relatively short time,thus promoting multivector synthetic jamming technology towards engineering applications.