3-D Multistatic Ground Penetrating Radar Imaging for Augmented Reality Visualization

Ground penetrating radar (GPR) is a useful instrument for smarter infrastructure applications, in particular, for the localization and mapping of underground infrastructure and other subsurface assets, due to its ability to sense metallic and nonmetallic buried objects. For instance, air-coupled, mu...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 58; H. 8; S. 5666 - 5675
Hauptverfasser:	Pereira, Mauricio, Burns, Dylan, Orfeo, Daniel, Zhang, Yu, Jiao, Liangbao, Huston, Dryver, Xia, Tian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	3-D feature extraction Algorithms Ambient noise Augmented reality augmented reality (AR) back-projection algorithm (BPA) Background noise Buried structures Data Data visualization enhancement filter Ground penetrating radar ground penetrating radar (GPR) imaging Imaging techniques Infrastructure Inspection Localization Mapping multistatic radar Noise reduction Object recognition Radar Radar imaging Receivers Scattering Scientific visualization Shape Signal processing Signal to noise ratio smart infrastructure Solid modeling Submarine pipelines Subsurface mapping synthetic aperture radar Three dimensional models Underground construction Underground utilities Visualization
ISSN:	0196-2892, 1558-0644
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Abstract	Ground penetrating radar (GPR) is a useful instrument for smarter infrastructure applications, in particular, for the localization and mapping of underground infrastructure and other subsurface assets, due to its ability to sense metallic and nonmetallic buried objects. For instance, air-coupled, multistatic GPR could potentially be employed to quickly produce subsurface maps for public and private stakeholders, enabling rational and more efficient planning of underground infrastructure inspection, maintenance, and construction. An application of interest in such context is a faster identification of underground utilities location and depth by innovative data visualization methods, such as augmented reality. A 3-D model of the subsurface asset is desirable for such applications. However, raw GPR data is often hard to interpret. Imaging algorithms are applied to improve GPR data readability and signal-to-noise ratio by focusing the spread energy. Here, a processing pipeline that takes raw 3-D multistatic GPR data as input and yields a 3-D model as output is proposed. Initially, a 3-D back-projection algorithm is applied to air-coupled, multistatic GPR data to recover buried target localization. An enhancement filter, tailored for tubular structures, is applied to reduce background noise and highlight structures of interest in the 3-D image. This process is successfully applied to three laboratory scenarios of plastic buried targets with different sizes and shapes.
AbstractList	Ground penetrating radar (GPR) is a useful instrument for smarter infrastructure applications, in particular, for the localization and mapping of underground infrastructure and other subsurface assets, due to its ability to sense metallic and nonmetallic buried objects. For instance, air-coupled, multistatic GPR could potentially be employed to quickly produce subsurface maps for public and private stakeholders, enabling rational and more efficient planning of underground infrastructure inspection, maintenance, and construction. An application of interest in such context is a faster identification of underground utilities location and depth by innovative data visualization methods, such as augmented reality. A 3-D model of the subsurface asset is desirable for such applications. However, raw GPR data is often hard to interpret. Imaging algorithms are applied to improve GPR data readability and signal-to-noise ratio by focusing the spread energy. Here, a processing pipeline that takes raw 3-D multistatic GPR data as input and yields a 3-D model as output is proposed. Initially, a 3-D back-projection algorithm is applied to air-coupled, multistatic GPR data to recover buried target localization. An enhancement filter, tailored for tubular structures, is applied to reduce background noise and highlight structures of interest in the 3-D image. This process is successfully applied to three laboratory scenarios of plastic buried targets with different sizes and shapes.
Author	Burns, Dylan Zhang, Yu Jiao, Liangbao Xia, Tian Huston, Dryver Pereira, Mauricio Orfeo, Daniel
Author_xml	– sequence: 1 givenname: Mauricio surname: Pereira fullname: Pereira, Mauricio organization: Department of Mechanical Engineering, The University of Vermont, Burlington, VT, USA – sequence: 2 givenname: Dylan surname: Burns fullname: Burns, Dylan organization: Department of Mechanical Engineering, The University of Vermont, Burlington, VT, USA – sequence: 3 givenname: Daniel surname: Orfeo fullname: Orfeo, Daniel organization: Department of Mechanical Engineering, The University of Vermont, Burlington, VT, USA – sequence: 4 givenname: Yu orcidid: 0000-0001-7440-3011 surname: Zhang fullname: Zhang, Yu organization: Department of Electronics and Safety, Delphi Automotive, Agoura Hills, CA, USA – sequence: 5 givenname: Liangbao surname: Jiao fullname: Jiao, Liangbao organization: Department of Telecommunications and Engineering, Nanjing Institute of Technology, Nanjing, China – sequence: 6 givenname: Dryver surname: Huston fullname: Huston, Dryver organization: Department of Mechanical Engineering, The University of Vermont, Burlington, VT, USA – sequence: 7 givenname: Tian orcidid: 0000-0002-4395-7350 surname: Xia fullname: Xia, Tian email: txia@uvm.edu organization: Department of Electrical and Biomedical Engineering, The University of Vermont, Burlington, VT, USA
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SubjectTerms	3-D feature extraction Algorithms Ambient noise Augmented reality augmented reality (AR) back-projection algorithm (BPA) Background noise Buried structures Data Data visualization enhancement filter Ground penetrating radar ground penetrating radar (GPR) imaging Imaging techniques Infrastructure Inspection Localization Mapping multistatic radar Noise reduction Object recognition Radar Radar imaging Receivers Scattering Scientific visualization Shape Signal processing Signal to noise ratio smart infrastructure Solid modeling Submarine pipelines Subsurface mapping synthetic aperture radar Three dimensional models Underground construction Underground utilities Visualization
Title	3-D Multistatic Ground Penetrating Radar Imaging for Augmented Reality Visualization
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