Multi-Frequency GPR Microwave Imaging of Sparse Targets Through a Multi-Task Bayesian Compressive Sensing Approach

TL;DR

This paper introduces a novel multi-frequency Bayesian compressive sensing method for high-resolution imaging of sparse underground targets using ground penetrating radar, effectively leveraging spectral diversity and sparsity priors.

Contribution

It develops a multi-task Bayesian compressive sensing approach that jointly processes multi-frequency GPR data and enforces sparsity, improving imaging accuracy of buried targets.

Findings

Enhanced imaging resolution for sparse targets.

Effective utilization of multi-frequency data.

Comparison shows superior performance over existing methods.

Abstract

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian Compressive Sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.