Norm-1 Regularized Consensus-Based ADMM for Imaging With a Compressive Antenna

This letter presents a novel norm-1-regularized, consensus-based imaging algorithm, based on the alternating direction method of multipliers (ADMM). This algorithm is capable of imaging metallic targets by using a limited amount of data. The distributed capabilities of the algorithm enable a fast im...

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Bibliographic Details
Published inIEEE antennas and wireless propagation letters Vol. 16; pp. 2362 - 2365
Main Authors Heredia-Juesas, Juan, Molaei, Ali, Tirado, Luis, Blackwell, William, Martinez-Lorenzo, Jose A.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Antennas
compressive antenna
Consensus alternating direction method of multipliers (ADMM)
Convex functions
Cost engineering
Image reconstruction
Imaging
Iterative methods
Minimum cost
norm-1 regularization
Radar imaging
real-time imaging
Reflector antennas
Sensors
Target recognition
Time compression
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Summary:This letter presents a novel norm-1-regularized, consensus-based imaging algorithm, based on the alternating direction method of multipliers (ADMM). This algorithm is capable of imaging metallic targets by using a limited amount of data. The distributed capabilities of the algorithm enable a fast imaging convergence. Recently, a compressive reflector antenna (CRA) has been proposed as a way to provide high sensing capacity with a minimum cost and complexity in the hardware architecture. The ADMM algorithm applied to the imaging capabilities of the CRA outperforms current state-of-the-art iterative reconstruction algorithms, such as Nesterov-based methods, in terms of computational cost, enabling the use of the CRA in quasi-real-time, compressive sensing imaging applications.
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2017.2718242