Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5965
Title: Sparsity enhanced fast subsurface imaging with GPR
Authors: Gürbüz, Ali Cafer
Keywords: Compressive sensing
GPR
Orthogonal matching pursuit
Sparse recontruction
Issue Date: 2010
Source: 13th Internarional Conference on Ground Penetrating Radar, GPR 2010, 21 June 2010 through 25 June 2010, Lecce, 81673
Abstract: Sparsity of a signal starts to become very important in many applications. In subsurface imaging, generally potential targets covers a small part of the total subsurface volume to be imaged, thus the targets are spatially sparse. Under this assumption it is shown that the subsurface imaging problem can be formulated as a dictionary selection problem which can be solved quickly using basis pursuit type algorithms compared to previously published convex optimization based methods. Spatial sparsity also indicates that the number of measurements (spatial or time/frequency) that GPR collects can be reduced, decreasing the data acquisition time. Orthogonal matching pursuit algorithm is used for reconstructing sparse subsurface images. Results show that the proposed method reduces time both in data acquisition and processing compared to previous methods with similar performance.
URI: https://doi.org/10.1109/ICGPR.2010.5550130
https://hdl.handle.net/20.500.11851/5965
ISBN: 9781424446049
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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