Robust imaging with electromagnetic waves in noisy environments

TL;DR

This paper develops a robust electromagnetic imaging method using random matrix theory to accurately locate small inclusions in noisy environments, demonstrating improved performance with multiple electric field components.

Contribution

It introduces a novel inversion technique based on random matrix theory that enhances imaging robustness in noisy electromagnetic data.

Findings

The method effectively locates inclusions despite noise.

Measuring multiple electric field components improves accuracy.

Numerical simulations validate the approach.

Abstract

We study imaging with an array of sensors that probes a medium with single frequency electromagnetic waves and records the scattered electric field. The medium is known and homogenous except for some small and penetrable inclusions. The goal of inversion is to locate and characterize these inclusions from the data collected by the array, which are corrupted by additive noise. We use results from random matrix theory to obtain a robust inversion method. We assess its performance with numerical simulations and quantify the benefit of measuring more than one component of the scattered electric field.