Widely-distributed Radar Imaging Based on Consensus ADMM

A widely-distributed radar system is a promising architecture to enhance imaging performance. However, most existing algorithms rely on isotropic scattering assumption, which is only satisfied in colocated radar systems. Moreover, due to noise and imaging model imperfections, artifacts such as layovers are common in radar images. In this paper, a novel l1 -regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting the spatial diversity in a widely-distributed radar system. By imposing the consensus constraints on the local images formed by the distributed antenna clusters and solving the resulting distributed optimization problem, the scenario's spatially-invariant common features are retained and the spatially-variant artifacts are mitigated in a data-driven fashion. The iterative procedure will finally converge to a high-quality global image in the consensus of all widely-distributed measurements. The proposed algorithm outperforms the existing joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of largescale imaging problems through its distributed and parallelizable optimization scheme.