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A Multiple Measurement Vector Approach for DOA Estimation

[ Vol. 10 , Issue. 3 ]

Author(s):

Seyyed Moosa Hosseini* and Ramezan A. Sadeghzadeh   Pages 216 - 222 ( 7 )

Abstract:


Background: Compressed Sensing (CS) is an emerging signal processing technique for signal acquisition and reconstruction which recently finds applications in array processing. Direction of Arrival (DOA) is a well-known problem in array signal processing which can be treated with methods based on compressed sensing.

Methods: In this work, a novel algorithm has been developed based on sparse multiple measurement vector model (MMV) to estimate DOAs of far-field and narrowband sources in linear arrays scenarios. The proposed algorithm exploits singular value decomposition denoising to enhance the reconstruction process.

Conclusion: Several simulations have been carried out to show the superior performance of proposed method in comparison to simultaneous orthogonal matching pursuit (S-OMP), Ι2,1 minimization and root-MUISC in both uniform linear array (ULA) and nonuniform linear array (NLA) scenarios.

Keywords:

Compressed sensing, direction of arrival, multiple measurement vector, non-uniform linear array, novel algorithm, signal processing.

Affiliation:

Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Faculty of Electrical Engineering, K.N.Toosi University of Technology, Tehran

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