Research On Linear Array Optimization Technology Based On Intelligent Optimization Algorithms

Array antenna is a technology that can work together through multiple antenna units to achieve directional radiation and receive signals.It can effectively suppress multipath propagation,improve the signal-to-noise ratio and increase the transmission capacity of the system.It has been widely used in many fields,such as radar,communication,Radio spectrum monitoring and other fields.However,the electromagnetic environment in space is becoming increasingly complex,and the array antenna may be affected by interference signals from various directions during signal transmission,which may seriously affect the performance of the system.In order to further enhance anti-interference performance and improve the system’s signal reception performance,research on array antenna optimization technology is becoming increasingly important.The optimization problem of array antennas involves selecting the phase,amplitude,and element position of the array antenna reasonably to ensure that the received signal has a specified radiation characteristic in a certain direction.This is a nonlinear multi parameter optimization problem aimed at meeting specific requirements such as low sidelobes,deep nulls,etc.for the radiation characteristics of the antenna.Traditional array antenna optimization methods can no longer meet the performance requirements of communication systems.The intelligent optimization algorithm has the advantages of fast Rate of convergence and simple implementation,and is gradually applied to the research of array antenna optimization.One of the important research directions is how to combine the advantages of multiple algorithms to construct more efficient and anti-interference array antenna optimization methods.This article focuses on how to obtain the optimal weight vector of adaptive beamforming algorithm in linear array scenarios,in order to minimize sidelobe peaks and deepen nulls.Secondly,research is conducted on how to select the position of array elements in sparse linear arrays to minimize the peak sidelobe as much as possible.The main content and innovative points of this article are as follows:In terms of adaptive beamforming algorithms,this article aims to reduce sidelobe peaks and deepen nulls,improving the reptile search algorithm by using Sobol sequences to initialize population positions to obtain more uniform initial positions;Adopting dynamic evolution factors to improve their adaptability to fitness functions;Synthesize two commonly used fitness functions and improve them to enhance the performance of beamforming patterns.The effectiveness of the algorithm in anti-interference performance was verified through simulation experiments.Compared to other algorithms,the improved algorithm not only reduces the peak sidelobe but also deepens the nulls,verifying the superiority of the improved reptile search algorithm performance.In terms of sparse layout array position selection,in order to address the problem of sidelobe peak suppression,a hunter prey optimization algorithm with better optimization ability is adopted.The selection,crossover,and mutation operations in genetic algorithm are added to the hunter prey optimization algorithm,so that the algorithm can avoid falling into local optima during the optimization process.By comparing simulation experiments under the same conditions,the superiority of the improved hunter prey optimization algorithm in sidelobe suppression was verified.