A Computational Electromagnetics and Sparsity-Based Feature Extraction Approach to Ground-Penetrating Radar Imaging

In this paper, a feature extraction technique based on the electromagnetic (EM) representation of radar signals is presented. In particular, we focus on ground-penetrating radar (GPR) imaging, where we model the backscatter from varying 2-D geometric shapes with arbitrary local coordinate rotations. Due to the electrically small nature of buried targets and the bending of the radar signal at the air-soil interface, we focus on exact methods to model the surface current density induced on scattering surfaces. Overcomplete basis sets are derived from the EM descriptions to represent the scene sparsely. From this proposed modeling framework, we devise a novel methodology to exploit the prediction of scattering behavior to extract features for classification from radar scenes when multiple buried scattering surfaces are present. We see that our method can identify and reconstruct buried scattering geometries in the presence of false targets that are brought about by the nonlinear nature of the exact EM modeling methods. A noniterative algorithm based on the conjugate of Green's function is developed to solve for the surface current in an unknown domain using multifrequency, multiaperture data. Our modeling and feature extraction algorithms are numerically validated for different target shapes buried in lossy soil profiles.

Automated summary

This paper presents a feature extraction technique based on the electromagnetic representation of radar signals, focusing on ground-penetrating radar imaging. The proposed modeling framework accurately models the surface current density induced on scattering surfaces. A novel methodology is devised to extract features for classification from radar scenes.