基于虚拟阵列的压缩波束形成方位估计方法  

Research on compressive beamforming based on virtual array

作　　者：宋海岩[1] 唐弢[1] 秦进平[1] SONG Haiyan;TANG Tao;QIN Jinping(College of Electrical and Information Engineering, Heilongiiang Institute of Technology, Harbin 150050, China)

机构地区：[1]黑龙江工程学院电气与信息工程学院,黑龙江哈尔滨150050

出　　处：《黑龙江工程学院学报》2018年第2期32-36,共5页Journal of Heilongjiang Institute of Technology

基　　金：黑龙江省普通本科高等学校青年创新人才培养计划(UNPYSCT-2015101);哈尔滨市科技创新人才研究专项资金项目(青年后备人才项目2016RQQXJ117);黑龙江省博士后资助项目(LBH-Z15045)

摘　　要：压缩感知又称为压缩采样技术,是一种新的信号感知或采样方法,已经在各个科学研究领域中崭露头角,尤其在空间目标方位估计(Direction Of Arrival,DOA)领域中引起研究学者的广泛关注。文中首先阐述压缩感知技术应用于目标方位估计的基本原理,并采用高效稳定的凸优化问题解算工具箱CVX进行目标方位的有效求解,在此基础之上,将压缩波束形成技术与虚拟阵列孔径理论相结合,提出一种基于虚拟阵列的压缩波束形成技术,以期获得更高的空间目标方位分辨能力。数值仿真结果表明了文中方法的有效性,尤其在低信噪比、相干源和小快拍数条件下,该方法能够分辨相近的空间目标,较传统方法具有更高的空间分辨能力。Compressive sensing,or compressive sampling（for short,CS）is a novel sensing/sampling paradigm which has already inspired some notable investigation in the context of Direction Of Arrival（DOA）estimation.This paper shows how CS can be applied in the DOA estimation and be solved by the well-established toolbox,CVX.In order to ensure a high spatial resolution,the conventional compressive beamforming formulation is further extended to a virtual array case.Numerical simulations illustrate the effectiveness of the DOA estimation algorithm based on CS.In addition,numerical tests also show that under some challenging scenarios such as low SNR,coherent arrivals and few snapshots,compressive beamforming based on virtually expanded array（for convenience,called V-CS）can distinguish closely spaced sources and have a higher resolving probability than conventional compressive beamforming.

关 键 词：压缩波束形成 方位估计 虚拟阵列 稀疏恢复 

分 类 号：TN911.7[电子电信—通信与信息系统] TB566[电子电信—信息与通信工程]