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IJMLC 2019 Vol.9(6): 768-773 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.6.871

The Development of Ground Penetrating Radar (GPR) Data Processing

Baso Maruddani and Efri Sandi

Abstract—Ground Penetrating Radar (GPR) is one of radar types that is often used to determine conditions inside or below some surface. GPR is also commonly used as a material evaluation tool by its trait as a non-destructive testing (NDT). One of the most important sections of GPR is signal processing system or GPR data processing that will filter all of the GPR survey results. Reflection signals gained by the radar antenna are then filtered to discover any objects located below the ground surface. The better the process in filtering data, the more accurate the GPR interprets the gained signal. This study aims to design a GPR data processing system that is sufficiently be able to interpret the gained signal so that it can accurately discover any objects located on the ground. This GPR data processing system is expected to work on a variety of frequencies and the application can be developed in various types. The aims of this study are designing and modifying the GPR data processing system.

Index Terms—GPR, processing, dewow, filtering, gain.

Baso Maruddani and Efri Sandi are with the Faculty of Engineering, Univeritas Negeri Jakarta, DKI Jakarta, Indonesia. Baso Maruddani is also with DJA Institute, DKI Jakarta, Indonesia (e-mail: basomaruddani@unj.ac.id, efri.sandi@unj.ac.id).

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Cite: Baso Maruddani and Efri Sandi, "The Development of Ground Penetrating Radar (GPR) Data Processing," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 768-773, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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