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Micro-Doppler Signature Detection and Recognition of UAVs Based on OMP Algorithm.

Shiqi Fan1, Ziyan Wu1, Wenqiang Xu1

  • 1Department of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study introduces a new method using the orthogonal matching pursuit (OMP) algorithm to suppress ground clutter for better unmanned aerial vehicle (UAV) detection. The technique effectively extracts micro-Doppler signals, crucial for identifying UAVs even in noisy environments.

Keywords:
OMP algorithmclutter suppressionidentification of UAVmicro-Dopplerradar signal processing

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Area of Science:

  • Radar Signal Processing
  • Unmanned Aerial Vehicle (UAV) Technology
  • Electromagnetics

Background:

  • Increasing use of UAVs necessitates robust identification and regulation methods.
  • Micro-Doppler signatures offer vital information for UAV recognition.
  • Ground clutter significantly hinders the detection of weak UAV micro-Doppler signals due to low altitude and small radar cross-section (RCS).

Purpose of the Study:

  • To propose and evaluate a novel clutter suppression method for UAV echo signals.
  • To enhance the extraction of weak micro-Doppler signals from strong ground clutter.
  • To enable reliable identification of UAVs using sparse representation techniques.

Main Methods:

  • Utilized a linear frequency modulated continuous wave (LFMCW) radar system.
  • Developed a clutter suppression technique based on the orthogonal matching pursuit (OMP) algorithm.
  • Employed sparse representation with environmental clutter dictionaries to cancel interference.
  • Analyzed processed signals in the time-frequency domain to identify UAVs based on rotor blade characteristics.

Main Results:

  • The OMP-based method demonstrated effectiveness in suppressing clutter power by -15 dB.
  • Successful extraction of micro-Doppler signals was achieved, even at a low signal-to-noise ratio (SNR) of -10 dB.
  • Field experiments confirmed the method's capability in identifying different UAVs.

Conclusions:

  • The proposed OMP algorithm offers a significant advancement in UAV micro-Doppler signal processing.
  • This method provides a robust solution for ground clutter cancellation in UAV detection.
  • The technique facilitates accurate UAV identification, addressing a critical need in surveillance and regulation.