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Improving the measurement resolution in BOTDR sensor with optimized wavelet denoising strategy.

Mustafa Essa Hamzah1, Mohd Saiful Dzulkefly Zan1, Abdulwahhab Essa Hamzah2

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This study introduces a new wavelet denoising (WD) and Lorentzian curve fitting (LCF) method to improve resolution in Brillouin optical time domain reflectometry (BOTDR). The optimized strategy significantly reduces measurement time and enhances sensing accuracy for distributed fiber applications.

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Area of Science:

  • Optical Engineering
  • Signal Processing
  • Fiber Optic Sensing

Background:

  • Signal averaging in Brillouin optical time domain reflectometry (BOTDR) creates a trade-off between measurement time and resolution.
  • Existing methods struggle to achieve high resolution without extensive averaging, limiting real-time applications.

Purpose of the Study:

  • To develop an optimized post-processing strategy integrating wavelet denoising (WD) with Lorentzian curve fitting (LCF) to mitigate the signal averaging trade-off in sub-meter differential cross-spectrum BOTDR (DCS-BOTDR).
  • To evaluate the performance of WD functions (Symlet, Daubechies, Coiflet, Biorthogonal Spline) with a 4-level decomposition on a six-core CPU using the single-program-multiple-data (SPMD) paradigm.

Main Methods:

  • Integration of wavelet denoising (WD) functions with traditional Lorentzian curve fitting (LCF).
  • Implementation of a 4-level decomposition using various WD functions (Symlet, Daubechies, Coiflet, Biorthogonal Spline).
  • Parallel processing on a six-core CPU utilizing a single-program-multiple-data (SPMD) paradigm for computational efficiency.

Main Results:

  • Achieved a 2.7 MHz Brillouin frequency shift (BFS) resolution with only 21,000 averages, compared to 56,000 averages for LCF alone.
  • Reduced required signal averages by 2.7 times.
  • Preserved 0.4 m spatial resolution and improved temperature resolution to 3°C over 1.21 km fiber with 14,000 averages.
  • Accelerated data processing speed by up to 4.8 times using a parallel multicore architecture.

Conclusions:

  • The proposed LCF + WD method offers a significant improvement in resolution and measurement time for DCS-BOTDR.
  • The training-free, straightforward strategy provides comparable BFS and temperature resolutions to machine learning methods without extensive datasets.
  • The parallel implementation is highly beneficial for real-time distributed sensing applications with constrained computational resources.