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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 58; no. 8; pp. 5666 - 5675
Main Authors: Pereira, Mauricio, Burns, Dylan, Orfeo, Daniel, Zhang, Yu, Jiao, Liangbao, Huston, Dryver, Xia, Tian
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.2968208