Next Article in Journal
Fluorescent Proteins as Genetically Encoded FRET Biosensors in Life Sciences
Previous Article in Journal
PUFKEY: A High-Security and High-Throughput Hardware True Random Number Generator for Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(10), 26267-26280;

Sparse Bayesian Learning for DOA Estimation with Mutual Coupling

School of Electrical and Information Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
National Mobile Communications Research Laboratory, Southeast University, 2 Sipailou Road, Nanjing 210096, China
School of Electronic and Information Engineering, Soochow University, 178 East Ganjiang Road, Suzhou 215006, China
Department of Automatic Control, Guangdong University of Technology, 100 Huanxi Road, Guangzhou 510006, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 23 August 2015 / Accepted: 10 October 2015 / Published: 16 October 2015
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [307 KB, uploaded 16 October 2015]   |  


Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise. View Full-Text
Keywords: Sparse Bayesian Learning (SBL); Direction-of-Arrival (DOA); Uniform Linear Array (ULA); mutual coupling Sparse Bayesian Learning (SBL); Direction-of-Arrival (DOA); Uniform Linear Array (ULA); mutual coupling

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Dai, J.; Hu, N.; Xu, W.; Chang, C. Sparse Bayesian Learning for DOA Estimation with Mutual Coupling. Sensors 2015, 15, 26267-26280.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top