NDLI: Array covariance matrix-based sparse Bayesian learning for off-grid direction-of-arrival estimation

Content Provider	IET Digital Library
Author	Zhao, Yonghong Zhang, Linrang Gu, Yabin
Abstract	An off-grid direction-of-arrival (DOA) estimation method using sparse Bayesian learning (SBL) based on an array covariance matrix is presented. By the vectorisation operator, a new off-grid model that has a wider aperture and can therefore detect an increased number of sources is built. A scheme of SBL is utilised to estimate off-grid DOAs without needing to know the number of sources. Owing to the extended aperture and off-grid model, the proposed method cannot only improve the angle resolution but also enhance the accuracy of DOA estimation. Simulation results demonstrate the efficiency of the proposed method and illustrate the performance improvement over the existing methods.
Starting Page	401
Ending Page	402
Page Count	2
ISSN	00135194
Volume Number	52
e-ISSN	1350911X
Issue Number	Issue 5, Mar (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/52/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2015.2931
Journal	Electronics Letters
Publisher Date	2016-01-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Angle Resolution Aperture Model Array Covariance Matrix-based SBL Array Covariance Matrix-based Sparse Bayesian Learning Belief Network Covariance Matrices Digital Signal Processing Direction of Arrival Estimation Knowledge Engineering Technique Learning in AI Off-grid Direction-of-arrival Estimation Off-grid DOA Estimation Method Signal Processing And Detection Vectorisation Operator
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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