English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents three distributed techniques to find a sparse solution ofthe underdetermined linear problem $\textbf{g}=\textbf{Hu}$ with a norm-1regularization, based on the Alternating Direction Method of Multipliers(ADMM). These techniques divide the matrix $\textbf{H}$ in submatrices by rows,columns, or both rows and columns, leading to the so-called consensus-basedADMM, sectioning-based ADMM, and consensus and sectioning-based ADMM,respectively. These techniques are applied particularly for millimeter-waveimaging through the use of a Compressive Reflector Antenna (CRA). The CRA is ahardware designed to increase the sensing capacity of an imaging system andreduce the mutual information among measurements, allowing an effective imagingof sparse targets with the use of Compressive Sensing (CS) techniques.Consensus-based ADMM has been proved to accelerate the imaging process andsectioning-based ADMM has shown to highly reduce the amount of information tobe exchange among the computational nodes. In this paper, the mathematicalformulation and graphical interpretation of these two techniques, together withthe consensus and sectioning-based ADMM approach, are presented. The imagingquality, the imaging time, the convergence, and the communication efficiencyamong the nodes are analyzed and compared. The distributed capabitities of theADMM-based approaches, together with the high sensing capacity of the CRA,allow the imaging of metallic targets in a 3D domain in quasi-real time with areduced amount of information exchanged among the nodes.

Consensus and Sectioning-based ADMM with Norm-1 Regularization for Imaging with a Compressive Reflector Antenna