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Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar

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  • 1École Polytechnique de Montréal, Montréal, QC H3T 1J4, Canada.

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Summary

This study demonstrates a 24 GHz radar system effectively detects and categorizes objects using a supervised machine learning model (Support Vector Machine - SVM). The system achieved high accuracy (96.6%) for real-time target identification without signal processing tools.

Keywords:
doppler frequencyin-phase/quadrature demodulatormachine learningmetronomemillimeter-wavemulti-class SVMsradar cross section (RCS)wavelet scalogram

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Area of Science:

  • Electrical Engineering
  • Machine Learning
  • Radar Systems

Background:

  • Object detection and classification are critical for radar sensor design.
  • Key parameters include target movement and radar cross-sections (RCS).
  • Existing methods may require complex signal processing toolboxes.

Purpose of the Study:

  • To evaluate the feasibility and effectiveness of a 24 GHz radar system for object detection.
  • To develop and apply a supervised machine learning model for target classification based on RCS.
  • To compare different Support Vector Machine (SVM) classification strategies.

Main Methods:

  • Utilized a 24 GHz radar with low-noise microwave amplifiers.
  • Trained a supervised machine learning model (SVM) using recorded target data.
  • Classified targets into four categories based on RCS and distance.
  • Compared one-against-all, one-against-one, and directed acyclic graph SVM methods.

Main Results:

  • Achieved approximately 96.6% accuracy and a 96.5% F1-score using the one-against-one SVM with an RFB kernel.
  • Demonstrated effective classification of objects at varying distances.
  • The proposed contactless radar and SVM approach showed high performance.

Conclusions:

  • The 24 GHz contactless radar system combined with an SVM algorithm is effective for real-time target detection and categorization.
  • The one-against-one SVM method with an RFB kernel provides superior classification performance.
  • This approach eliminates the need for a signal processing toolbox for real-time applications.