Research On The Method Of Optimization Of Thinned Array Antennas

In the practical application of the array antenna,there are always some limits to the main lobe width and the side lobe level of the array antenna to meet the spatial resolution and anti-interference performance requirements of the radar system.Researches have shown that the structure of the array antenna and the position of the array elements are closely related to the antenna beam shape,sidelobe level and directionality of the antenna.The lower sidelobe level means higher resolution of the radar system and less clutters.Therefore,how to optimize the antenna array to obtain lower sidelobe levels quickly and effectively has become a major topic of this thesis.The thinned array antenna can save the cost of device and simplify the antenna structure by achieving higher resolution with the same aperture size and fewer array elements.Therefore,it has become a research hotspot in the studies of antenna array.For large array antennas,the traditional statistical methods to optimize the array to reduce sidelobe levels is already not suited.In recent years,with the improvement of computer computing ability,heuristic optimization algorithms(intelligent optimization algorithms)have been widely used in thinned array optimization as they have very high efficiency and parallelism.This thesis focuses on the technical research of optimization techniques for linear array antennas and planar array antennas.The genetic algorithm and particle swarm optimization algorithm in the intelligent optimization algorithm are improved and combined with thinned array optimization to obtain the lower relative sidelobe level as low as possible.Firstly,the basic principle and mathematical model of linear array antenna and planar array antenna are introduced.According to the principle of direction product of antenna pattern,the antenna pattern of two arrays is given,and the mathematical optimization model of thinned array is given.Secondly,the basic concepts,algorithm operations and application scenarios of genetic algorithm are introduced.The crossover and mutation algorithm operator in genetic algorithm are redesigned.A new dynamic adaptive genetic algorithm is proposed and compared with the standard genetic algorithm.Thirdly,researches on the particle swarm optimization algorithm is carried on and the catfish effect is applied to the particle swarm optimization algorithm to form an improved Boolean binary catfish particle swarm algorithm,which is applied to thinned linear array and thinned planar array optimization.Under the experimental conditions,a lower maximum relative sidelobe level was obtained.