Optimization Of Subarray Locations For Distributed Arrays

Unlike traditional large-scale array antennas,distributed array antennas are composed of multiple sub-arrays and cooperate with each other,therefore,there are advantages such as high maneuverability and high flexibility,low manufacturing complexity,low maintenance cost,good expandability,and high reliability.However,distributed array antennas also have the drawback that the peak sidelobe level is too high,therefore,the antenna system has a large error in parameter estimation,so the study of array pattern synthesis of low peak sidelobe levels is of great significance.Array patterning is an important property of array antennas.For the problem that the peak sidelobe level of the distributed array antenna is too high,this article takes a distributed array of multiple sub-arrays of the same structure as an example,optimize the position of the subarray,reduce the peak sidelobe level of the distributed array.The main work and innovations of this paper are as follows:(1)For the problem that the peak sidelobe level of the distributed array antenna is too high,Firstly,this paper studies the genetic algorithm and apply it to the subarray position optimization problem of distributed array antennas.Then according to the simulation results,the shortcomings and defects of traditional genetic algorithms are pointed out and a corresponding improved genetic algorithm is proposed,focus on improving the two operations of crossover and mutation in the algorithm.Before performing the crossover operation,the similarity detection is first performed to avoid "inbreeding".In the process of mutation,the idea of “adaptation” is adopted,and the value of the mutation probability is changed according to the fitness of the population.According to the experimental results,1.A distributed array antenna optimized for sub-array position by using a genetic algorithm and an improved genetic algorithm has a lower peak sidelobe level than a distributed array antenna having an equal distance from adjacent sub-arrays.2.The improved genetic algorithm has faster convergence speed and higher global search ability than traditional genetic algorithm.(2)Compared with the shortcomings of genetic algorithms and improved genetic algorithms,differential evolution algorithms have the advantages of high convergence efficiency and strong global search performance,therefore,this paper proposes to use the differential evolution algorithm to optimize the sub-array position of the distributed array antenna,so that the peak sidelobe level of the distributed array is reduced.And aiming at the shortcomings of differential evolution algorithm,an improved differential evolution algorithm is proposed.The key point is to propose corresponding improvement measures for the three operations of mutation,crossover and selection.In order to improve the convergence efficiency of the algorithm and the enhanced global search ability,the improved differential evolution algorithm uses a combination of two different differential evolution strategies.In the cross-operation part,the adaptive approach is adopted,and the value of the cross-probability is dynamically adjusted based on the current fitness value of the population.For the improvement of the selection operation,not only the individuals with high fitness values are retained to the next generation population,but the individuals with low fitness values are also probable to be retained,so that the diversity of individuals in the population is improved.According to the experimental results,1.The distributed array antennas optimized by sub-array position using differential evolution algorithm and improved differential evolution algorithm have reduced the peak sidelobe level.2.The four algorithms used in this paper,the improved differential evolution algorithm has higher convergence efficiency and better global search ability.