Accelerated Consensus ADMM for Widely Distributed Radar Imaging

Abstract

Widely distributed radar systems are expected to enhance radar imaging performance due to their ability to capture diverse spatial scattering proprieties. Optimization-based sub-aperture imaging methods are used to adopt the isotropic scattering assumption within a narrow angular extent and reconstruct the scene image by fusing sub-aperture images. A previously proposed method based on consensus alternating direction method of multipliers (CADMM) provides a joint reconstruction of sub-aperture images along with a global image that represents the anisotropic scene. In this paper, we propose a modified version of CADMM which features lower complexity and faster convergence. By gradually learning the scene support during the iterative reconstruction, our proposed algorithm focuses on the image portion that contains the scattering targets and updates the sub-images accordingly. It also reduces the communication cost between the distributed sensors which need to exchange local image updates during CADMM iterations.