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Threat Assessment of Buried Objects Using Single-Frequency Microwave Measurements.

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  • 1Electronics and Communication Engineering Department, Istanbul Technical University, 34469 Istanbul, Turkey.

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A new lightweight neural network model with a microwave detection system accurately identifies buried objects using real-world scattering parameter (S-parameter) data. This robust system achieves high accuracy for defense and security applications.

Keywords:
buried object detectionmicrowave systemsneural network

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Area of Science:

  • Applied Physics
  • Machine Learning
  • Geophysics

Background:

  • Buried object detection is crucial for security and unexploded ordnance (UXO) clearance.
  • Existing methods often require complex systems or extensive training data.
  • Microwave-based sensing offers a non-invasive approach to subsurface analysis.

Purpose of the Study:

  • To develop a lightweight neural network model for buried object identification.
  • To integrate this model with a microwave detection system using real-world measurements.
  • To evaluate the model's performance against established deep learning architectures.

Main Methods:

  • Utilized 16x16 scattering parameter (S-parameter) measurements from real-world data.
  • Transformed S-parameter data into a 256-dimensional feature vector.
  • Developed and trained a lightweight neural network architecture on the feature vectors.

Main Results:

  • The model achieved 99.83% accuracy, 0.989 F1 score, and 0.979 recall in distinguishing hazardous objects.
  • Outperformed baseline Convolutional Neural Network (CNN), Deep Residual Network (DRN), and EfficientNet models.
  • Demonstrated robustness and practical relevance due to training on real-world measurements.

Conclusions:

  • The proposed lightweight neural network and microwave detection system is highly effective for buried object identification.
  • The approach offers a computationally efficient and accurate solution for defense and security.
  • Real-world data integration enhances the model's reliability for practical deployment.