Distributed Radar Imaging Based on Accelerated ADMM

TL;DR

This paper introduces an accelerated ADMM-based method for distributed radar imaging that improves convergence speed, reduces computational complexity, and lowers communication costs in sensor networks.

Contribution

It proposes a heuristic acceleration technique for ADMM in distributed radar imaging, enhancing efficiency and practicality over previous methods.

Findings

Faster convergence of the proposed method.

Reduced computational complexity.

Lower communication costs during iterations.

Abstract

The ability of widely distributed radar systems to capture diverse spatial scattering properties substantially improves radar imaging performance. Traditional imaging methods leverage regularized optimization techniques to reconstruct sparse images from local sensors and later combine them to create a global image. Alternatively, we proposed in an earlier work a joint reconstruction technique based on two problem formulations according to the optimization framework of the Alternating Direction Method of Multipliers (ADMM). The joint reconstruction of the global image offers faster convergence, flexible implementation, and a general distributed reconstruction framework. However, despite its benefits, ADMM framework still exhibits a slow convergence rate, making its employment in some contexts impractical. In this paper, we introduce a heuristic method to accelerate the convergence of the previously proposed ADMM formulations based on the gradual elimination of the already converged pixels in accordance with a predetermined criterion. In addition to reducing running time, the accelerated implementation offers reduced computational complexity and lower communication cost between the sensors during iterative updates.