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Volume 7 (2013) : Issue 1, Feb

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

A reduced l 2–l 1 model with an alternating minimisation algorithm for support recovery of multiple measurement vectors

Content Provider	IET Digital Library
Author	Du, Xinpeng Chen, Daiqiang Cheng, Lizhi
Abstract	The authors address the problem of support recovery with multiple measurement vectors (MMV) in this study. The scale of an MMV is reduced by using the singular value decomposition technique, and a novel l 2–l 1 minimisation model with two variables for the reduced MMV is proposed. Then a new alternating minimisation algorithm based on the alternating direction method of multipliers is presented. They prove the globally convergence property of the presented algorithm. Several numerical simulations both on random data and for direction-of-arrival estimation are conducted to evaluate the performance of the proposed method for support recovery of MMV.
Starting Page	112
Ending Page	119
Page Count	8
ISSN	17519675
Volume Number	7
e-ISSN	17519683
Issue Number	Issue 2, Apr (2013)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/7/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2012.0078
Journal	IET Signal Processing
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Alternating Direction Method Alternating Minimisation Algorithm Convergence of Numerical Method Direction of Arrival Estimation Globally Convergence L2-l1 Minimisation Model Linear Algebra Minimisation MMV Multiple Measurement Vector Support Recovery Numerical Analysis Numerical Simulation Optimisation Technique Random Data Reduced L2-l1 Model Signal Processing And Detection Signal Processing Theory Singular Value Decomposition Singular Value Decomposition Technique Vector
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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