Imaging of buried objects from experimental backscattering time dependent measurements using a globally convergent inverse algorithm

TL;DR

This paper presents a globally convergent inverse algorithm for imaging buried objects using experimental backscattering data, effectively addressing data mismatch and ground reflection challenges, and demonstrating accurate dielectric constant estimation on real data.

Contribution

The authors develop and test a new data preprocessing approach combined with a globally convergent inverse algorithm for imaging buried objects from experimental measurements.

Findings

Successful dielectric constant estimation on experimental data

Effective removal of ground surface reflections from measurements

Good accuracy achieved on 25 data sets, including blind targets

Abstract

We consider the problem of imaging of objects buried under the ground using backscattering experimental time dependent measurements generated by a single point source or one incident plane wave. In particular, we estimate dielectric constants of those objects using the globally convergent inverse algorithm of Beilina and Klibanov. Our algorithm is tested on experimental data collected using a microwave scattering facility at the University of North Carolina at Charlotte. There are two main challenges working with this type of experimental data: (i) there is a huge misfit between these data and computationally simulated data, and (ii) the signals scattered from the targets may overlap with and be dominated by the reflection from the ground's surface. To overcome these two challenges, we propose new data preprocessing steps to make the experimental data to be approximately the same as the simulated ones, as well as to remove the reflection from the ground's surface. Results of total 25 data sets of both non blind and blind targets indicate a good accuracy.