  This paper presents three distributed techniques to find a sparse solution of
the underdetermined linear problem $\textbf{g}=\textbf{Hu}$ with a norm-1
regularization, 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-based
ADMM, sectioning-based ADMM, and consensus and sectioning-based ADMM,
respectively. These techniques are applied particularly for millimeter-wave
imaging through the use of a Compressive Reflector Antenna (CRA). The CRA is a
hardware designed to increase the sensing capacity of an imaging system and
reduce the mutual information among measurements, allowing an effective imaging
of sparse targets with the use of Compressive Sensing (CS) techniques.
Consensus-based ADMM has been proved to accelerate the imaging process and
sectioning-based ADMM has shown to highly reduce the amount of information to
be exchange among the computational nodes. In this paper, the mathematical
formulation and graphical interpretation of these two techniques, together with
the consensus and sectioning-based ADMM approach, are presented. The imaging
quality, the imaging time, the convergence, and the communication efficiency
among the nodes are analyzed and compared. The distributed capabitities of the
ADMM-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 a
reduced amount of information exchanged among the nodes.
