# A Linear Model for Microwave Imaging of Highly Conductive Scatterers

## TL;DR

This paper introduces a linear, versatile model for microwave imaging of highly conductive objects, leveraging joint sparse regularization to improve resolution without prior information or model approximation.

## Contribution

A novel linear model based on multiple measurement vectors for inverse scattering, utilizing joint sparse structure enforcement for enhanced imaging of conductive scatterers.

## Key findings

- Higher resolving ability than linear sampling methods
- Effective with synthetic and Fresnel data
- No prior information or scattering model approximation needed

## Abstract

In this paper, a linear model based on multiple measurement vectors model is proposed to formulate the inverse scattering problem of highly conductive objects at one single frequency. Considering the induced currents which are mostly distributed on the boundaries of the scatterers, joint sparse structure is enforced by a sum-of-norm regularization. Since no \textit{a priori} information is required and no approximation of the scattering model has been made, the proposed method is versatile. Imaging results with transverse magnetic and transverse electric polarized synthetic data and Fresnel data demonstrate its higher resolving ability than both linear sampling method and its improved version with higher, but acceptable, computational complexity.