A Sparse Representation-Based DOA Estimation Algorithm With Separable Observation Model

Conventional sparse representation (SR)-based direction-of-arrival (DOA) estimation algorithms suffer from high computational complexity. To be specific, a wide angular range and a large-scale array will enlarge the scale of the spatial observation matrix, which results in huge computation cost for...

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Bibliographic Details
Published in:IEEE antennas and wireless propagation letters 2015, Vol.14, p.1586-1589
Main Authors: Zhao, Guanghui, Shi, Guangming, Shen, Fangfang, Luo, Xi, Niu, Yi
Format: Article
Language:English
Subjects:
Arrays
Azimuth
Direction-of-arrival (DOA)
Direction-of-arrival estimation
Estimation
planar array
Signal to noise ratio
sparse representation
Transmission line matrix methods
Vectors
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Summary:Conventional sparse representation (SR)-based direction-of-arrival (DOA) estimation algorithms suffer from high computational complexity. To be specific, a wide angular range and a large-scale array will enlarge the scale of the spatial observation matrix, which results in huge computation cost for DOA estimation. In this letter, a new efficient DOA estimation algorithm based on the separable sparse representation (SSR-DOA for short) is derived, in which a separable structure for spatial observation matrix is introduced to reduce the complexity. Besides, a dual-sparsity strategy is engaged to make the algorithm tractable. Experimental results show that high resolution performance can be obtained efficiently by the proposed algorithm.
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2015.2413814