计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 271-276.doi: 10.11896/jsjkx.191200139

所属专题: 网络通信

• 计算机网络 • 上一篇    下一篇

基于原子范数最小化的二维稀疏阵列波达角估计算法

卢爱红1,2, 郭艳1, 李宁1, 王萌1, 刘杰1   

  1. 1 陆军工程大学通信工程学院 南京210007
    2 苏州经贸职业技术学院 江苏 苏州215009
  • 收稿日期:2019-12-16 出版日期:2020-05-15 发布日期:2020-05-19
  • 通讯作者: 郭艳(guoyan_1029@sina.com)
  • 作者简介:lahnet@163.com
  • 基金资助:
    国家自然科学基金(61871400);江苏省自然科学基金(BK20171401)

Direction-of-arrival Estimation with Two-dimensional Sparse Array Based on Atomic NormMinimization

LU Ai-hong1,2, GUO Yan1, LI Ning1, WANG Meng1, LIU Jie1   

  1. 1 College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China
    2 Suzhou Institute of Trade and Commerce,Suzhou,Jiangsu 215009,China
  • Received:2019-12-16 Online:2020-05-15 Published:2020-05-19
  • About author:LU Ai-hong,born in 1981,postgraduate Ph.D.Her research interests focus on array signal processing,wireless communications,and compressive sensing.
    GUO Yan,born in 1971,Ph.D,professor,Ph.D supervisor.Her main research interests include compressive sensing,MIMO and cognitive radio.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871400) and Natural Science Foundation of Jiangsu Province,China(BK20171401)

摘要: 基于二维稀疏平面阵列的波达角(Direction-of-arrival,DOA)估计问题在第五代移动通信大规模多输入多输出阵列的应用中日益重要。无网格稀疏重构技术促进了DOA估计问题的发展,原子范数理论则使得DOA估计的超分辨率得到进一步的提高。文中研究了多个方向的频谱稀疏信号入射到二维稀疏阵列时的DOA估计问题。为了准确、成对地识别出所有入射信号的仰角和方向角,提出了一种基于多个测量矢量(Multiple Measurement Vectors,MMV)的二维原子范数算法,并用半正定规划进行求解。所提算法将二维DOA估计问题中的压缩感知理论从单个测量矢量拓展到多个测量矢量,从而有效利用MMV的联合稀疏性。数值仿真结果表明,随着MMV矢量的增长,可识别的信源个数增加,稀疏阵列中物理传感器所占比例降低到30%,DOA估计误差也显著降低,并且在信噪比增大时,所提算法能够取得很好的收敛效果。

关键词: 波达角, 多个测量矢量, 二维稀疏阵列, 联合稀疏性, 原子范数最小化

Abstract: Direction-of-arrival (DOA) estimation based on two-dimensional planar sparse array is increasingly important in the application of massive MIMO arrays of 5G.The gridless sparse reconstruction technology promotes the development of DOA estimation research,and the super-resolution of DOA estimation methods has been advanced with the atomic norm theory.In this paper,DOA estimation is studied when spectrally-sparse signals from multiple directions are incidented on a two-dimensional sparse array.In order to accurately identify the azimuth and elevation angles of all incident signals in pairs,a two-dimensional atomic norm approach based on multiple measurement vectors (MMV) is proposed,and can be solved by semidefinite programming.The proposed algorithm extends compressive sensing of two-dimensional DOA estimation from a single measurement vector to multiple measurement vectors,so as to effectively use the joint sparsity of MMV.Numerical simulation results show that,as the MMV vector grows,the number of identifiable sources increases,the proportion of physical sensors in the sparse array decreases to 30%,the DOA estimation error decreases significantly,and the proposed algorithm can achieve a good convergence effect when the signal-to-noise ratio increases.

Key words: Atomic norm minimization, Direction-of-arrival, Joint sparsity, Multiple measurement vectors, Two-dimensional planar sparse array

中图分类号: 

  • TN911.7
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