Widely-distributed Radar Imaging Based on Consensus ADMM

TL;DR

This paper introduces a novel distributed radar imaging algorithm using consensus ADMM that effectively reduces artifacts and improves image quality by leveraging spatial diversity in widely-distributed radar systems.

Contribution

It proposes an $l_1$-regularized consensus ADMM algorithm that mitigates artifacts and enhances imaging performance in distributed radar systems, outperforming previous methods.

Findings

Outperforms joint sparsity-based algorithms in artifact mitigation.

Reduces computational and storage requirements for large-scale imaging.

Converges to high-quality global images through distributed optimization.

Abstract

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.