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

Hosseini, Seyyed Moosa, Sadeghzadeh, Ramazan Ali and Virdee, Bal Singh (2017) DOA estimation using multiple measurement vector model with sparse solutions in linear array scenarios. EURASIP Journal on Wireless Communications and Networking, 58. pp. 1-9. ISSN http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-017-0838-y

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Abstract / Description

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.

Item Type:	Article
Uncontrolled Keywords:	Compressed sensing, Direction of arrival, Multiple measurement vector, Nonuniform linear array
Subjects:	600 Technology > 620 Engineering & allied operations
Department:	School of Computing and Digital Media
Depositing User:	Bal Virdee
Date Deposited:	05 Apr 2017 07:45
Last Modified:	05 Apr 2017 07:45
URI:	https://repository.londonmet.ac.uk/id/eprint/1205

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