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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
Abstract
This work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling approach for buried objects characterization, (ii) construction of the surrogate model in a computationally efficient manner using small training datasets, (iii) development of a novel deep learning method, time-frequency regression model (TFRM), that employes raw signal (with no pre-processing) to achieve competitive estimation performance. The presented approach is favourably benchmarked against the state-of-the-art regression techniques, including multilayer perceptron (MLP), Gaussian process (GP) regression, support vector regression machine (SVRM), and convolutional neural network (CNN).
Citations
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8
CrossRef
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0
Web of Science
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9
Scopus
Authors (5)
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Reyhan Yurt
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Hamid Torpi
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Peyman Mahouti
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Ahmet Kizilay
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/ACCESS.2023.3243132
- License
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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IEEE Access
no. 11,
pages 13309 - 13323,
ISSN: 2169-3536 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Yurt R., Torpi H., Mahouti P., Kizilay A., Kozieł S.: Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models// IEEE Access -Vol. 11, (2023), s.13309-13323
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/access.2023.3243132
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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