Optical Technique, Volume. 51, Issue 1, 94(2025)

Research on improvement of OFDR strain sensing accuracy based on cross correlation spectrum moving average

QI Qingyun1, XU Hang1, BAI qing1, LIANG Changshuo1, WU Luxuan1, WANG Yu1, and JIN Baoquan1,2、*
Author Affiliations
  • 1Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030600, China
  • 2College of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030600, China
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    OFDR is prone to phase noise during long distance and large strain sensing, which makes the strain sensing accuracy poor or unable to restore the real strain change. The cross correlation spectrum moving average method is used to process the original cross correlation spectrum of OFDR to improve the accuracy of strain sensing. The mechanism of strain demodulation interference noise generation is described, and the principle of OFDR strain sensing accuracy improvement based on cross-correlation spectrum moving average is analyzed. The OFDR strain sensing system is built, and experimental verification is carried out on 103.8m sensing fiber. The experimental results show that when the sweep frequency range is 60nm and the average sliding block size is 1×10, the strain sensing with a spatial resolution of 1.33cm and a maximum value of 4000με can be achieved in the 103.8m fiber under test, and the standard deviation of wavelength offset in the strain region is significantly reduced. It is proved that the cross-correlation spectrum moving average method can effectively improve the strain demodulation precision of OFDR system.

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    QI Qingyun, XU Hang, BAI qing, LIANG Changshuo, WU Luxuan, WANG Yu, JIN Baoquan. Research on improvement of OFDR strain sensing accuracy based on cross correlation spectrum moving average[J]. Optical Technique, 2025, 51(1): 94

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    Paper Information

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    Received: Jul. 31, 2024

    Accepted: Feb. 18, 2025

    Published Online: Feb. 18, 2025

    The Author Email: Baoquan JIN (jinbaoquan@tyut.edu.cn)

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