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Space-frequency analysis with parallel computing in a phase-sensitive optical time-domain reflectometer distributed

Xiaonan Hui, Taihang Ye, Shilie Zheng

    Applied Optics
    |October 17, 2014
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    Summary

    A new graphics processing unit (GPU)-based method enhances phase-sensitive optical time-domain reflectometer (ϕ-OTDR) systems. This GPU parallel computing enables real-time space-frequency analysis, significantly reducing false alarms in distributed sensor systems.

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    Area of Science:

    • Optical Engineering
    • Signal Processing
    • Distributed Sensing

    Background:

    • Traditional difference value methods in phase-sensitive optical time-domain reflectometer (ϕ-OTDR) systems are prone to false alarms.
    • Real-time analysis of frequency distribution is crucial for improving the accuracy of distributed sensor systems.

    Purpose of the Study:

    • To develop a graphics processing unit (GPU)-based parallel computing method for real-time space-frequency analysis in ϕ-OTDR systems.
    • To enhance the performance and reduce computational load for long-range distributed sensing.

    Main Methods:

    • Implementation of a GPU-based parallel computing approach for multichannel fast Fourier transform (FFT).
    • Application of real-time space-frequency analysis on sensing fiber up to 16 km.
    • Utilizing an entry-level GPU to manage computational demands.

    Main Results:

    • Significant reduction in the time required for multichannel FFT using GPU parallel computing.
    • Demonstrated capability for sensing fiber lengths up to 16 km.
    • Reduced central processing unit (CPU) load from 70% to under 20%.
    • Successful two-point space-frequency analysis, simultaneously identifying vibration locations and frequencies.
    • Real-time output of space-frequency spectra with 16.3 m spatial and 2.25 Hz frequency resolution.

    Conclusions:

    • The proposed GPU-based parallel computing method effectively enables real-time space-frequency analysis for ϕ-OTDR systems.
    • This approach significantly improves system performance, reduces false alarms, and lowers CPU workload.
    • The method is practical for long-range sensing applications using accessible hardware.