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

Abstract

In this paper, a feature extraction technique based on the electromagnetic representation of radar signals is presented. In particular, we focus on ground penetrating radar imaging, where we model the backscatter from varying two dimensional 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 electromagnetic descriptions to represent the scene in a sparse manner. 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 from the nonlinear nature of the exact electromagnetic modelling 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 multi-frequency, multi-aperture data. Our modeling and feature extraction algorithms are numerically validated for different target shapes buried in lossy soil profiles.