Transactions on Machine Intelligence

Transactions on Machine Intelligence

Landmine Detection by Correlation Method in Different Environments

Document Type : Original Article

Authors
A. Gharamohammadi 1
Y. Norouzi 2
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
2 Department of Electrical Engineering, Assistance prof. of Electrical Eng, Amirkabir University of Technology, Tehran, Iran
10.47176/TMI.2022.87
Abstract
This study aims to detect landmines in various environments by analyzing the scattering parameters obtained via Ground Penetrating Radar (GPR). The GPR system captures the scattering parameter, which serves as the key indicator of subsurface anomalies. A reference signal is first acquired from a simplified environment where a landmine is embedded in isolation. Signals from more complex or cluttered environments are then measured and compared against this reference. The presence of a landmine alters the scattering characteristics of the medium, influencing the measured parameter. By evaluating the similarity between the reference signal and the signals collected from the target environment, it is possible to infer the existence of a landmine. This similarity is quantified using a correlation function, which effectively highlights matching patterns. The strength of this approach lies in the distinctive and invariant nature of the scattering parameter, which provides a reliable basis for detection. The proposed method offers a practical and efficient solution for landmine detection, especially in scenarios where signal clarity and robustness are essential. Its reliance on scattering parameter uniqueness ensures consistent performance, making it a promising tool for real-world applications in humanitarian demining and subsurface anomaly detection.
Keywords
Ground Penetrating RADAR
Landmine Detection
Scattering Parameter
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Transactions on Machine Intelligence
Volume 5, Issue 2
Spring 2022
Pages 87-96

  • Receive Date 03 February 2022
  • Revise Date 09 March 2022
  • Accept Date 11 June 2022