Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (5): 271-276.doi: 10.11896/jsjkx.191200139

Special Issue: Network and communication

• Computer Network • Previous Articles     Next Articles

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)

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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

CLC Number: 

  • TN911.7
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