Abstract
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), l2,1 minimization, and root-MUISC.
Highlights
Compressed sensing (CS) is a novel paradigm shift in sampling and signal acquisition which has attracted considerable attention for application in wireless communications, signal processing, and array processing [1,2,3]
5 Simulations We considered the problem of direction of arrival (DOA) estimation in both uniform linear array and nonuniform linear array scenarios
The results show the proposed method consumes less time to calculate singular value decomposition (SVD) values than simultaneous orthogonal matching pursuit (S-OMP) because it deals with a reduced matrix size of Yred ∈ CM × K while S-OMP has to work with a larger matrix of Y ∈ CM × L
Summary
Compressed sensing (CS) is a novel paradigm shift in sampling and signal acquisition which has attracted considerable attention for application in wireless communications, signal processing, and array processing [1,2,3]. Some new approaches have been introduced that exploit the spatial sparsity of source signals to obtain DOA estimations [7, 8] These methods are based on defining a sampling grid on the angular solution space and solving the conventional single measurement CS problem. In this formulation, the DOAs correspond to the elements of support set Ω.
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