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Title: Compressive sensing for subsurface imaging using ground penetrating radar
Authors: Gürbüz, Ali Cafer
McClellan, James H.
Scott, Waymond R., Jr.
Keywords: Compressive sensing
Synthetic aperture
Ground penetrating radar (GPR)
l(1) Minimization
Subsurface imaging
Source localization
Issue Date: 2009
Publisher: Elsevier
Abstract: The theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of non-adaptive linear measurements by solving a convex l(1) minimization problem. This paper presents a novel data acquisition system for wideband synthetic aperture imaging based on CS by exploiting sparseness of point-like targets in the image space. Instead of measuring sensor returns by sampling at the Nyquist rate, linear projections of the returned signals with random vectors are used as measurements. Furthermore, random sampling along the synthetic aperture scan points can be incorporated into the data acquisition scheme. The required number of CS measurements can be an order of magnitude less than uniform sampling of the space-time data. For the application of underground imaging with ground penetrating radars (GPR), typical images contain only a few targets. Thus we show, using simulated and experimental GPR data, that sparser target space images are obtained which are also less cluttered when compared to standard imaging results. (C) 2009 Elsevier B.V. All rights reserved.
ISSN: 0165-1684
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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