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

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Yurt R., TORPİ H., MAHOUTİ P., KIZILAY A., Koziel S.

IEEE Access, cilt.11, ss.13309-13323, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1109/access.2023.3243132
  • Dergi Adı: IEEE Access
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.13309-13323
  • Anahtar Kelimeler: Buried object characterization, ground penetrating radar (GPR), surrogate modeling, microwave modeling, artificial intelligence, A-scan data analysis
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

AuthorThis 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).