使用重加权原子范数最小化的稀疏可重构直线阵列设计  

Design of sparse reconfigurable array using reweighted atomic norm minimization

作　　者：黎子皓 吴敏 郝程鹏[1,2] 侯朝焕 LI Zihao;WU Min;HAO Chengpeng;HOU Chaohuan(Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国科学院声学研究所,北京100190 [2]中国科学院大学,北京100049

出　　处：《声学学报》2023年第6期1174-1188,共15页Acta Acustica

基　　金：国家自然科学基金项目(62001468,61971412)资助。

摘　　要：为克服网格失配问题并提升阵列性能,提出了使用重加权原子范数最小化的稀疏可重构直线阵列设计方法,将稀疏可重构直线阵列设计问题表示为多测量矢量稀疏优化模型,并通过重加权原子范数最小化算法解算出阵元位置和阵元激励。区别于经典压缩感知方法,该方法借助原子范数理论建立了阵元数量、阵元位置和阵元激励联合优化的无网格稀疏优化模型,从而可以克服网格失配问题,并提升阵列波束图的匹配精度。仿真实验表明,与压缩感知类方法相比,重加权原子范数最小化算法可以设计出波束匹配精度高一个数量级的稀疏可重构直线阵列。To overcome the grid mismatch issue and enhance the performance of beam patterns,a sparse reconfigurable linear array design method based on reweighted atomic norm minimization is proposed.This method formulates the sparse reconfigurable linear array design problem as multiple measurement vectors sparse optimization model.It solves for the element positions and element excitations of the sparse reconfigurable linear array by the reweighted atomic norm minimization.Unlike conventional compressed sensing-based methods,this approach leverages atomic norm theory to establish a gridless sparse optimization model that jointly optimizes the number,positions,and excitations of array elements.As a result,it can overcome the grid mismatch problem and enhance the matching accuracy of the array beam pattern.Simulation experiments demonstrate that compared to compressed sensing methods,the reweighted atomic norm minimization method can design sparse reconfigurable linear arrays with an order of magnitude higher beam matching accuracy.

关 键 词：网格失配 稀疏可重构直线阵列 原子范数 无网格稀疏优化 匹配精度 

分 类 号：TB566[交通运输工程—水声工程] P714[理学—物理]