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An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

Hongqiang Xiong1, Baizhou An2, Boyang Sun2,3

  • 1College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China.

Sensors (Basel, Switzerland)
|May 25, 2024
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Summary

The synchrosqueezing generalized phase-shifting S-transform (SS-GPST) enhances ground penetrating radar (GPR) data analysis by improving spectral focusing and reducing noise. This new method offers superior accuracy and speed for GPR applications.

Keywords:
S-transformground penetrating radarsynchrosqueezingtunnel inspection

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Area of Science:

  • Geophysics
  • Signal Processing

Background:

  • The S-transform is crucial for time-frequency analysis in ground penetrating radar (GPR) data processing.
  • Spectrum leakage in the S-transform degrades accuracy in GPR applications like target identification and imaging.
  • Synchrosqueezing effectively mitigates spectrum leakage.

Purpose of the Study:

  • Introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST) for improved GPR data analysis.
  • Address the compatibility between S-transform and synchrosqueezing through phase-shifting.
  • Enhance spectral energy focusing and resolution in GPR data.

Main Methods:

  • Developed the synchrosqueezing generalized phase-shifting S-transform (SS-GPST).
  • Utilized phase-shifting to integrate S-transform and synchrosqueezing.
  • Employed a generalized parameter for spectral focusing and resolution enhancement.

Main Results:

  • SS-GPST demonstrated superior focusing, spectral resolution, and signal reconstruction accuracy and speed compared to other methods in synthetic tests.
  • SS-GPST accurately identified thin layers (3.0-3.5 cm) in GPR tunnel detection data.
  • SS-GPST proved effective for GPR data denoising through inverse transform due to reduced interference components.

Conclusions:

  • SS-GPST offers high-resolution time-frequency spectra with strong focusing and minimal interference.
  • The method shows significant adaptability for various GPR data processing tasks, including denoising and thin layer detection.
  • SS-GPST presents potential as a replacement for the traditional S-transform in GPR analysis.