NDLI: Off-grid sparse DOA estimation based iterative reweighted linear interpolation in spatial coloured noise

Content Provider	IET Digital Library
Author	Yuan, Jiawen Zhang, Gong Liu, Wenbo Wang, Xinhai
Abstract	An off-grid sparse direction-of-arrival (DOA) estimation algorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the DOA estimation precision present in unknown spatial coloured noise. The authors start by developing an off-grid sparse model based on linear interpolation with reweighted coefficient, which is a trade-off between tangent and secant offset, to guarantee an optimal approximation for off-grid signals. Next, the authors formulate the DOA estimation problem as solving the off-grid sparse model and, finally, the off-grid sparse model is addressed under the general framework of sparse Bayesian learning (SBL). Additional noise in IRLI is spatially coloured for calculating its statistical properties, which is different from SBL relying on the spatial white noise assumption. Numerical results with the limited snapshots and the low signal-to-noise ratio validate the algorithm by comparing with other algorithms.
Starting Page	573
Ending Page	575
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 11, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.0381
Journal	Electronics Letters
Publisher Date	2020-03-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Direction of Arrival Estimation Direction-of-arrival Estimation Algorithm Interpolation Interpolation And Function Approximation IRLI Iterative Method Iterative Reweighted Linear Interpolation Coefficient Learning in AI Low Signal-to-noise Ratio Numerical Analysis Off-grid Signal Approximation Off-grid Sparse DOA Estimation Problem SBL Signal Processing And Detection Signal Processing Theory Sparse Bayesian Learning Spatial White Noise Assumption Statistical Analysis Statistical Property Statistics Unknown Spatial Coloured Noise White Noise
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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