Φ-OTDR disturbance localization of communication optical cable based on moving variance average and spectral subtraction
|更新时间:2025-10-16
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Φ-OTDR disturbance localization of communication optical cable based on moving variance average and spectral subtraction
Vol. 44, Issue 3, Pages: 211-218(2025)
作者机构:
国网江苏省电力有限公司泰州供电分公司,江苏,泰州,225300
作者简介:
基金信息:
DOI:
CLC:TM773;TN247
Published:2025
稿件说明:
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SHEN Wei. Φ-OTDR disturbance localization of communication optical cable based on moving variance average and spectral subtraction[J]. 2025, 44(3): 211-218.
DOI:
SHEN Wei. Φ-OTDR disturbance localization of communication optical cable based on moving variance average and spectral subtraction[J]. 2025, 44(3): 211-218.DOI:
Φ-OTDR disturbance localization of communication optical cable based on moving variance average and spectral subtraction
摘要
针对相位敏感光时域反射(phase sensitive-optical time domain reflectometry
To address the issue of low signal-to-noise ratio in disturbance localization on power communication optical cables caused by environmental noise in phase sensitive-optical time domain reflectometry (Φ-OTDR) systems
the Φ-OTDR localization technique is investigated based on moving variance averaging and spectral subtraction. A coherent detection Φ-OTDR system is built to connect optical fibers and optical cables to carry out galloping simulation experiments. The backward Rayleigh scattering signal is acquired by using the system and the scattering signal is demodulated to obtain the amplitude signal curve. By selecting an appropriate number of curves and an optimal step size
the amplitude signal curves are processed using moving variance average to generate a preliminary disturbance localization map. To suppress ambient noise outside the localization region
noise reduction using spectral subtraction is performed to achieve enhanced localization. A comparative analysis is conducted between spectral subtraction and two alternative noise reduction methods-wavelet decomposition and empirical mode decomposition. The results demonstrate that spectral subtraction significantly outperforms the other two algorithms in localization effectiveness
achieving an SNR improvement exceeding 20 dB while maintaining a computational time as low as 0.58 s.