基于CIES-CA的水声阵列多目标方位估计技术  

Multi-target azimuth estimation of underwater acoustic array based on a coprime array with compressed inter-element spacing(CIES-CA)

作　　者：王辛 郭龙祥 生雪莉[1,2,3] 殷敬伟 李佳馨[1,2,3] WANG Xin;GUO Longxiang;SHENG Xueli;YIN Jingwei;LI Jiaxin(Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China;Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China)

机构地区：[1]哈尔滨工程大学水声技术重点实验室,黑龙江哈尔滨150001 [2]海洋信息获取与安全工业和信息化部重点实验室(哈尔滨工程大学),黑龙江哈尔滨150001 [3]哈尔滨工程大学水声工程学院,黑龙江哈尔滨150001

出　　处：《哈尔滨工程大学学报》2021年第2期246-252,共7页Journal of Harbin Engineering University

基　　金：国家重点研发计划(2018YFC1405906);国家自然科学基金项目(51779061).

摘　　要：为了使用较少阵元数的水声阵列实现多目标方位估计,本文针对基于子阵间距压缩的互质阵列进行研究分析。利用子空间分类算法与虚拟阵元法,在考虑阵列误差的情况下分析多目标方位估计性能。通过数值验证,在低信噪比、高阵列误差的情况下,基于子阵压缩的互质阵性能明显优于均匀直线阵与原型互质阵,且压缩倍数越大性能越高。In order to realize the multi-objective azimuth angle estimation using the underwater acoustic array with a small number of array elements,this paper analyzes the underwater acoustic coprime array with compressed inter-element spacing.Based on a consideration of the array error of the underwater acoustic array,this paper analyzes the azimuth estimation performance of the coprime array using a subspace classification algorithm and virtual array element method.Through simulation verification,under the condition of low signal-to-noise ratio and high array error,the performance of the coprime array with compressed inter-element spacing is significantly better than the uniform linear array and the prototype coprime array,and the larger the compression factor,the higher the estimated performance.

关 键 词：互质阵 子阵间距压缩 多目标方位估计 子空间分类算法 虚拟阵元法 多目标方位分辨概率 多目标方位估计误差 通道幅相误差 阵元位置误差 

分 类 号：TB566[交通运输工程—水声工程]