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CEEMDAN-LWT De-Noising Method for Pipe-Jacking Inertial Guidance System Based on Fiber Optic Gyroscope.

Yutong Zu1,2,3, Lu Wang1,2,3, Yuanbiao Hu1

  • 1School of Engineering and Technology, China University of Geosciences, Beijing 100083, China.

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|February 24, 2024
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Summary
This summary is machine-generated.

A novel hybrid de-noising method combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Lifting Wavelet Transform (LWT) effectively reduces noise in fiber optic gyroscope signals for pipe jacking. This approach significantly improves navigation accuracy by suppressing environmental disturbances.

Keywords:
CEEMDANFOGLWTnoise reductionpipe-jacking

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Area of Science:

  • Geotechnical Engineering
  • Signal Processing
  • Navigation Systems

Background:

  • Long-distance curved pipe jacking relies on inertial guidance systems, often using fiber optic gyroscopes (FOGs).
  • Environmental factors like vibration, electromagnetism, and temperature introduce significant random noise into FOG signals, degrading guidance accuracy.
  • Effective noise reduction is crucial for reliable FOG-based pipe jacking guidance.

Purpose of the Study:

  • To propose and validate a hybrid de-noising method for fiber optic gyroscope (FOG) signals used in pipe jacking.
  • To enhance the accuracy and reliability of inertial guidance systems by mitigating environmental noise.
  • To improve the measurement precision of pipe-jacking attitude angles (pitch, roll, heading).

Main Methods:

  • A hybrid de-noising approach termed CEEMDAN-LWT (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise - Lifting Wavelet Transform) is introduced.
  • FOG signals are decomposed using CEEMDAN to obtain intrinsic mode functions (IMFs), with relevant components selected based on correlation coefficients.
  • High-frequency IMFs are further processed using LWT for enhanced resolution, followed by soft threshold de-noising and signal reconstruction.

Main Results:

  • The CEEMDAN-LWT method demonstrated superior de-noising performance compared to standalone CEEMDAN or LWT, evidenced by improved SNR, RMSE, and Deviation metrics.
  • Experimental validation on pipe-jacking dynamic and environmental interference tests confirmed the method's effectiveness and adaptivity.
  • Navigation accuracy significantly improved, with absolute errors for pitch, roll, and heading angles reduced by 39.86%, 59.45%, and 14.29%, respectively.

Conclusions:

  • The CEEMDAN-LWT hybrid de-noising method effectively suppresses random errors in FOG signals caused by environmental disturbances.
  • This technique substantially enhances the measurement accuracy of pipe-jacking attitude, crucial for precise guidance in complex underground environments.
  • The proposed method offers a robust solution for improving the performance of inertial guidance systems in demanding civil engineering applications.