3D Random Antenna Array Design For Suppressing Sidelobe Level Based On Swarm Intelligence Algorithms

The application of wireless transmission systems in various fields has led to continuous advances in the research of antenna arrays.Based on this research,smart antenna technology has been developed.Since the energy of the antenna radiation signal is limited,the sidelobe level of the pattern itself is the key to its energy loss.Therefore,how to optimize the sidelobe level of the antenna array is an important issue in the smart antenna technology.Sidelobe level optimization solutions for many linear,circular 2D arrays and coplanar arrays have been presented,but sidelobe level optimizationof 3D random antenna arrays is a problem that needs further investigation.According to the radiation characteristics of the antenna array,a mathematical model of 3D random antenna array is constructed.The general antenna array pattern function is a 2D array expression and the spherical coordinate form is not conducive to the numerical analysis of the array element position.Based on the general direction graph function,the array factor expression of the 3D random antenna array is derived by Cartesian coordinate system transformation.When the array elements are assumed to be omnidirectional elements,the pattern of the antenna array can be analyzed by the array factor.The maximum sidelobe level is the main part of the signal sidelobe energy loss,so a mathematical model of the maximum sidelobe level is constructed,with the decibel value ofthe maximumsidelobelevel as the objective function,which means that the maximum value of the antenna array pattern other than the first zero.Since the conventional traditional pattern optimization methods such as the Taylor synthesis method,the Chebyshev synthesis method and the Fourier transform method have more parameter setting restrictions,and generally only apply to certain specific uniformly distributed antenna array forms,the algorithm further combining the good performance of today's group intelligent optimization algorithm for numerical optimization in various applications,we chose to use the group intelligent optimization method to solve the problem constructed in this paper.This paper chooses to use the Particle Swarm Optimization algorithm,which is often used as a comparison algorithm,and two kinds of Firefly Algorithm and Cuckoo Search,which are popular today and have been proved to be better by various problems,to analyze the sidelobe of the antenna array.According to the analysis of the objective function,the optimization form is divided into two types,one is to optimize only the array element excitation,and the other is to optimize the array element excitation and the array element position at the same time.According to the principle and general implementation steps of the three group intelligent algorithms,the specific implementation methods of each algorithm in the two optimization methods are given.After comparison of simulation experiments,the three group intelligent optimization algorithms have a good inhibitory effect on the maximum sidelobe level of the 3D random antenna array.By analyzing the data,the optimal optimization algorithm of stability and optimization effect is obtained.Further,through the experiment of two optimization parameters,a better optimization parameter scheme is obtained,and the defined problem is solved.