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One-Dimensional Convolutional Neural Network for Object Recognition Through Electromagnetic Backscattering in the

Mohammad Hossein Zadeh1, Marina Barbiroli1, Simone Del Prete1

  • 1Department of Electrical, Electronic, and Information Engineering "G. Marconi", University of Bologna, 40136 Bologna, Italy.

Sensors (Basel, Switzerland)
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Summary

This study introduces a new machine learning approach for object recognition using electromagnetic backscattering. The method achieves high accuracy in object detection and recognition, offering a potential alternative to vision-based systems.

Keywords:
Convolutional Neural Networksdeep learningelectromagnetic backscatteringmeasurementsobject recognition

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Area of Science:

  • Electromagnetics
  • Machine Learning
  • Signal Processing

Background:

  • Item recognition traditionally relies on radar or computer vision.
  • Machine and deep learning, particularly Convolutional Neural Networks (CNNs), are now state-of-the-art for image-based recognition.
  • Vision-based methods face limitations like data unavailability, poor image quality, and privacy concerns.

Purpose of the Study:

  • To investigate a novel machine learning object recognition approach using electromagnetic backscattering in the frequency domain.
  • To address the limitations of vision-based object recognition systems.

Main Methods:

  • Employed a 1D Convolutional Neural Network (CNN) to analyze electromagnetic backscattered signals.
  • Collected data through backscattering measurements in the millimeter-wave (mmWave) band within controlled environments.
  • Utilized signal generators and spectrum analyzers for reliable data acquisition.

Main Results:

  • Achieved 100% accuracy in object detection.
  • Attained 84% accuracy in object recognition for two object classes.
  • Identified a trade-off between accuracy and processing speed based on signal bandwidth and frequency steps.

Conclusions:

  • Electromagnetic-based object recognition shows promise as a complement or alternative to vision-based systems, especially when vision is impractical.
  • The proposed approach demonstrates flexibility and potential for real-time applications due to adjustable accuracy-speed trade-offs.