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Exploiting sparsity and field conditioning in subsurface microwave imaging of nonweak buried targets
Author(s) -
Bevacqua Martina,
Crocco Lorenzo,
Donato Loreto Di,
Isernia Tommaso,
Palmeri Roberta
Publication year - 2016
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1002/2015rs005904
Subject(s) - ground penetrating radar , lossy compression , microwave imaging , computer science , inverse scattering problem , inverse problem , compressed sensing , microwave , representation (politics) , radar , basis (linear algebra) , scattering , remote sensing , algorithm , geology , artificial intelligence , optics , mathematics , physics , telecommunications , mathematical analysis , geometry , politics , law , political science
An efficient inverse scattering strategy is proposed to achieve dielectric characterization of buried objects in lossy soils. The approach takes advantage of Virtual Experiments and Compressive Sensing to obtain quantitative reconstructions of nonweak targets which are nonsparse in the pixel representation basis, commonly adopted in microwave imaging. In addition, an original strategy is adopted to overcome the relevant information lack arising when data are gathered under aspect‐limited configurations, such as in ground penetrating radar (GPR) surveys. The proposed strategy significantly outperforms the results achievable with the “state of the art” standard approaches since it allows to achieve nearly optimal reconstructions within a linear framework and without increasing the overall computational burden. Numerical examples with simulated data are given to show the feasibility of the proposed strategy.