DOA estimation using multiple measurement vector model with sparse solutions in linear array scenarios

被引：1
|
作者：
Hosseini, Seyyed Moosa [1 ]
Sadeghzadeh, R. A. [1 ]
Virdee, Bal Singh [2 ]
机构：
[1] KN Toosi Univ Technol, Fac Elect Engn, Tehran, Iran
[2] London Metropolitan Univ, Ctr Commun Technol, London N7 8DB, England
来源：
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING | 2017年
关键词：
Compressed sensing; Direction of arrival; Multiple measurement vector; Nonuniform linear array; SIGNAL RECONSTRUCTION; MATCHING PURSUIT;
D O I：
10.1186/s13638-017-0838-y
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A novel algorithm is presented based on sparse multiple measurement vector (MMV) model for direction of arrival (DOA) estimation of far-field narrowband sources. The algorithm exploits singular value decomposition denoising to enhance the reconstruction process. The proposed multiple nature of MMV model enables the simultaneous processing of several data snapshots to obtain greater accuracy in the DOA estimation. The DOA problem is addressed in both uniform linear array (ULA) and nonuniform linear array (NLA) scenarios. Superior performance is demonstrated in terms of root mean square error and running time of the proposed method when compared with conventional compressed sensing methods such as simultaneous orthogonal matching pursuit (S-OMP), l(2),(1) minimization, and root-MUISC.
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收藏
页数：9
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