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Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference.

Xiaolin Ma1, Running Zhao2, Xinhua Liu3

  • 1Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, and Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China. maxiaolin0615@whut.edu.cn.

Sensors (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

This study introduces a robust human motion classification system designed to overcome interference from non-target micro-motions. The novel approach achieves high accuracy in complex wireless environments.

Keywords:
continuous wave radarhuman motion classificationmicro-Doppler effectnon-target micro-motion interference

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Area of Science:

  • Radar Signal Processing
  • Human Motion Analysis
  • Machine Learning

Background:

  • Micro-Doppler effect enables human motion classification.
  • Non-target micro-motion interference degrades classification performance by creating cross-terms.
  • Existing methods lack robustness against such interference.

Purpose of the Study:

  • To develop a human motion classification system resilient to non-target micro-motion interference.
  • To enhance the accuracy of human motion identification in cluttered wireless environments.
  • To address the limitations of current methods in handling signal aliasing and cross-terms.

Main Methods:

  • Utilized a continuous wave radar transceiver (USRP N210) in a low-frequency band.
  • Applied Empirical Mode Decomposition and S-transform for interference removal and signal enhancement.
  • Developed an Energy Aggregation method based on S-method to suppress cross-terms and noise.
  • Extracted features and employed Bagged Trees for classifying four distinct human motions.

Main Results:

  • Successfully removed non-target micro-motion interference.
  • Improved time-frequency resolution of the raw radar signals.
  • Suppressed cross-terms and background noise effectively.
  • Achieved 97.3% classification accuracy in scenarios with interference.

Conclusions:

  • The proposed system demonstrates high effectiveness in classifying human motions amidst non-target micro-motion interference.
  • The combination of signal processing techniques and machine learning provides a robust solution.
  • This work offers a significant advancement for reliable human motion detection in challenging environments.