  In this paper, we present a novel approach to reconstruct a unique image of
an observed scene with widely distributed radar sensors. The problem is posed
as a constrained optimization problem in which the global image which
represents the aggregate view of the sensors is a decision variable. While the
problem is designed to promote a sparse solution for the global image, it is
constrained such that a relationship with local images that can be
reconstructed using the measurements at each sensor is respected. Two problem
formulations are introduced by stipulating two different establishments of that
relationship. The proposed formulations are designed according to consensus
ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided
accordingly as iterative algorithms. We drive the explicit variable updates for
each algorithm in addition to the recommended scheme for hybrid parallel
implementation on the distributed sensors and a central processing unit. Our
algorithms are validated and their performance is evaluated exploiting Civilian
Vehicles Dome data-set to realize different scenarios of practical relevance.
Experimental results show the effectiveness of the proposed algorithms,
especially in cases with limited measurements.
