摘要
为了降低稀布阵列的峰值旁瓣电平并抑制稀布阵列的栅瓣,提出了一种多目标粒子群与凸优化相结合的方向图综合算法。该算法将多目标粒子群优化(Multi-objective particles swarm optimization,MOPSO)作为全局搜索器,凸优化算法作为局部搜索器来搜索最优解,优化的变量不仅是阵元的权值,而且还加入了阵元位置这一参数,从而能够提供更多的自由度来控制稀布阵列的性能。基于30阵元的稀布圆形阵列的仿真结果表明:与单纯使用MOPSO算法相比,本文提出的用MOPSO算法优化阵元位置,凸优化算法优化阵元权值的联合优化算法,得到的栅瓣和峰值旁瓣电平都小于-19.3dB。
To reduce the peak side-lobe level of the sparse array pattern effectively and suppress the sparse array grating lobe at the same time,apattern 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.
出处
《数据采集与处理》
CSCD
北大核心
2017年第5期980-987,共8页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61371170)资助项目
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
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
中央高校基本科研业务费专项资金(NJ20140010)资助项目
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
sparse array
convex optimization
multi-objective particles swarm
grating lobe suppression
peak side-lobe level
