Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models - Publikacja - MOST Wiedzy

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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

Abstrakt

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).

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
IEEE Access nr 11, strony 13309 - 13323,
ISSN: 2169-3536
Język:
angielski
Rok wydania:
2023
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/access.2023.3243132
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

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