  A widely-distributed radar system is a promising architecture to enhance
radar imaging performance. However, most existing algorithms rely on isotropic
scattering assumption, which is only satisfied in collocated radar systems.
Moreover, due to noise and imaging model imperfections, artifacts such as
layovers are common in radar images. In this paper, a novel $l_1$-regularized,
consensus alternating direction method of multipliers (CADMM) based algorithm
is proposed to mitigate artifacts by exploiting a widely-distributed radar
system's spatial diversity. By imposing the consensus constraints on the local
images formed by distributed antenna clusters and solving the resulting
distributed optimization problem, the scenario's spatial-invariant common
features are retained. Simultaneously, the spatial-variant artifacts are
mitigated, and it will finally converge to a high-quality global image in the
consensus of all distributed measurements. The proposed algorithm outperforms
the joint sparsity-based composite imaging (JSC) algorithm in terms of
artifacts mitigation. It can also reduce the computation and storage burden of
large-scale imaging problems through its distributed and parallelizable
optimization scheme.
