An Efficient Sparse Representation Algorithm for Direction-of-Arrival Estimation
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Date
2013-09
Authors
Sun, Lei
Wang, Huali
Xu, Guangjie
ORCID
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Mark
Journal Title
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Publisher
Společnost pro radioelektronické inženýrství
Abstract
This paper presents an efficient sparse representation approach to direction-of-arrival (DOA) estimation using uniform linear arrays. The proposed approach constructs the jointly sparse model in real domain by exploiting the properties of centro-Hermitian matrices. Subsequently, DOA estimation is realized via the sparse Bayesian learning (SBL) algorithm. Further, the pruning threshold of SBL is adaptively selected to speed up the basis pruning rate. Simulation results demonstrate that the proposed approach achieves an improved performance and enjoys computational efficiency as compared to the state-of-the-art l1-norm-based DOA estimators especially in scenarios with small sample size and low signal-to-noise ratio.
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Citation
Radioengineering. 2013, vol. 22, č. 3, s. 834-840. issn 1210-2512
http://www.radioeng.cz/fulltexts/2013/13_03_0834_0840.pdf
http://www.radioeng.cz/fulltexts/2013/13_03_0834_0840.pdf
Document type
Peer-reviewed
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en