数据采集与处理2017，Vol.32Issue(5)：980-987,8.DOI:10.16337/j.1004-9037.2017.05.015
基于MOPSO与凸优化算法的稀布圆阵列方向图优化
Sparse Circular Array Pattern Optimization Based on MOPSO and Convex Optimization
摘要
Abstract
To reduce the peak side-lobe level of the sparse array pattern effectively and suppress the sparse array grating lobe at the same time,a pattern synthesis algorithm using multi-objective particles swarm optimization (MOPSO) combined with convex optimization algorithm is presented in this paper.We take MOPSO as a global search and convex optimization as the local search to search for the optimal solution.In this search,the optimization variables include not only the weights of the array,but also the positions of the array,which can provide more freedom to control the performance of the sparse array.Simulation of a sparse circular array model of thirty elements reveals that compared with MOPSO algorithm alone,the proposed algorithms which uses MOPSO and convex optimization to optimize the positions and the weights of the array respectively,can obtain the grating lobes and the peak side-lobe level of lower than -19.3 dB at the same time.关键词
稀布阵列/凸优化/多目标粒子群/栅瓣抑制/峰值旁瓣电平Key words
sparse array/convex optimization/multi-objective particles swarm/grating lobe suppression/peak side-lobe level分类
信息技术与安全科学引用本文复制引用
曹爱华,李海林,马守磊,周建江..基于MOPSO与凸优化算法的稀布圆阵列方向图优化[J].数据采集与处理,2017,32(5):980-987,8.基金项目
国家自然科学基金(61371170)资助项目 （61371170）
南京航空航天大学研究生创新基地(实验室)开放基金(kfjj20150403,kfjj20160404)资助项目 （实验室）
中央高校基本科研业务费专项资金(NJ20140010)资助项目 （NJ20140010）
雷达成像与微波光子技术教育部重点实验室(南京航空航天大学)资助项目. （南京航空航天大学）
