Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

TL;DR

This paper introduces a novel ADMM-based method for reconstructing a global radar image from widely distributed sensors, effectively handling limited measurements through iterative algorithms validated on real data.

Contribution

It proposes two new formulations using consensus and sharing ADMM for distributed radar imaging, with explicit variable updates and hybrid implementation schemes.

Findings

Algorithms effectively reconstruct images with limited data

Validated on Civilian Vehicles Dome dataset

Show improved performance over existing methods

Abstract

In this paper, we present a novel approach to reconstruct a unique image of an observed scene with widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated exploiting Civilian Vehicles Dome data-set to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.