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Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency

Dingkun Huang1, Xiaopeng Yan1, Xinhong Hao1

  • 1Technology on Electromechanical Dynamic Control Laboratory, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

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Summary

This study introduces a novel method for identifying multiple radiation source signals in low signal-to-noise ratio (SNR) environments. The approach effectively sorts and recognizes signals using cyclic statistics, time-frequency imaging, and the YOLOv5 deep learning model.

Keywords:
CWD time-frequency analysisYOLOv5 modelcyclic stationary analysisnoise suppressionradiation source signal sorting and identification

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Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Electromagnetics

Background:

  • Traditional signal recognition methods struggle with multiple emitters in low signal-to-noise ratio (SNR) environments.
  • Accurate identification of radiation sources is crucial for electronic warfare and surveillance.

Purpose of the Study:

  • To develop a robust method for classifying and identifying multiple emitter signals in low SNR conditions.
  • To enhance the real-time performance of multi-emitter signal recognition.

Main Methods:

  • Utilized low-order cyclic statistics for denoising and feature extraction of radiation source signals.
  • Employed Chirp-rate Weighted Distribution (CWD) time-frequency analysis to generate signal representations.
  • Integrated the YOLOv5 deep learning model for efficient signal classification and sorting.

Main Results:

  • The proposed method successfully dissociates, labels, and sorts multi-emitter signal features in the time-frequency domain.
  • Achieved effective noise suppression for multisource signals at low SNR by controlling cyclic frequency.
  • Demonstrated high real-time performance and accurate identification of diverse signal modulation types.

Conclusions:

  • The developed method offers a significant improvement for multi-emitter signal recognition in challenging low SNR environments.
  • The combination of cyclic statistics, CWD imaging, and YOLOv5 provides a powerful tool for real-time signal identification.