相敏型光时域反射系统信噪比提升

王子豪; 刘志凯; 冯玉祥; 张成龙; 吕立冬

doi:10.37188/CO.2024-0122

相敏型光时域反射系统信噪比提升

doi: 10.37188/CO.2024-0122

cstr: 32171.14.CO.2024-0122

安徽工业大学电气与信息工程学院, 安徽马鞍山 243002

基金项目: 国家自然科学基金项目（No. 51977001）

详细信息

作者简介:
吕立冬（1982—），男，四川邻水人，博士，副教授，硕士生导师，2005年于西安工业大学获得学士学位，2009年于中国科学院国家天文台南京天文光学技术研究所获得硕士学位，2012于南京大学获得博士学位，主要研究方向为电力设备状态监测与诊断、分布式光纤传感、能源互联网等，现主要从事光纤传感技术及其在智能电网方面的研究。E-mail：lvlidong@ahut.edu.cn

中图分类号: TP212.9;TN212
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出版历程
- 收稿日期: 2024-07-02
- 修回日期: 2024-08-30
- 录用日期: 2024-10-09
- 网络出版日期: 2024-11-28

Improvement of signal-to-noise ratio for phase-sensitive optical time-domain reflecting system

School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, China

Funds: Supported by National Natural Science Foundation of China (No. 51977001)

More Information

Corresponding author: lvlidong@ahut.edu.cn

摘要

摘要:
相位敏感型光时域反射系统（Φ-OTDR）的灵敏度受激光器的相位噪声、掺铒光纤放大器的自发辐射噪声、光电探测器的散粒噪声及热噪声等系统固有噪声和环境随机噪声的制约，因此，本文研究光时域反射数据的降噪算法，在不降低系统频率响应范围的条件下提高系统的信噪比。本文提出Savitzky-Golay平滑算法，选择固定长度的滑动窗口，对窗口内的光时域反射数据进行降噪处理，同时保持数据的采样频率，并搭建实验系统进行验证。实验结果显示：采用Savitzky-Golay平滑算法，系统的信噪比相对于原始信号逐差法的信噪比提高了5.41 dB，与常用的累加平均算法、滑动平均算法相比信噪比分别提升3.39 dB和5.05 dB。结果表明：Savitzky-Golay平滑算法可提高Φ-OTDR系统的灵敏度和准确度，使其能够精准地感知微小振动事件，以降低系统误报率。
- 相位敏感光时域反射仪 /
- Savitzky-Golay平滑算法 /
- 信噪比 /
- 阈值定位
Abstract:
The sensitivity of the phase-sensitive optical time-domain reflecting (Φ-OTDR) system is limited by the system’s intrinsic noise, such as the laser’s phase noise, the erbium-doped fiber amplifier’s spontaneous emission noise, and the photodetector’s shot and thermal noise, as well as random environment of noise. Therefore, we investigate the noise reduction algorithm based on the optical time-domain reflecting data to improve the system’s signal-to-noise ratio (SNR) without degrading its frequency response range. Furthermore, we propose a Savitzky-Golay smoothing algorithm by selecting a slidable fixed-length window to process OTDR data for the SNR improvement while maintaining the system sampling frequency. Then, we built the experimental system to demonstrate the results. The experimental results show that by using the Savitzky-Golay smoothing algorithm, the SNR of the system is improved by 5.41 dB relative to the difference method with the original data, and the SNR is improved by 3.39 dB and 5.05 dB, compared to the commonly used cumulative averaging method and sliding averaging method, respectively. It is demonstrated that the Savitzky-Golay smoothing algorithm can improve the sensitivity and accuracy of the Φ-OTDR system, which helps to sense weak vibration events accurately and reduce false alarm rates.
- phase sensitive optical time domain reflectometer /
- Savitzky-Golay smoothing algorithm /
- SNR /
- threshold positioning

HTML全文

图 1 Φ-OTDR系统结构图

Figure 1. Schematic diagram of the Φ-OTDR system

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图 2 原始OTDR曲线。(a)未加载事件；(b)加载事件

Figure 2. Original OTDR traces. (a) No vibration event loaded; (b) loading vibration events

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图 3 无振动事件时的逐差曲线。(a)差值曲线；(b)峰值曲线

Figure 3. Differential traces in the absence of vibration events. (a) Difference traces; (b) peak trace

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图 4 逐差法曲线

Figure 4. Traces obtained by difference method

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图 5 不同算法无振动事件时的逐差峰值曲线。(a)原始曲线；(b)累加平均算法；(c)滑动平均算法；(d) S-G平滑算法

Figure 5. Differential peak traces obtained by different algorithms in the absence of vibration events. (a) Original trace; (b) cumulative average algorithm; (c) moving average algorithm; (d) S-G smoothing algorithm

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图 6 不同算法处理OTDR曲线。(a)原始曲线；(b)累加平均算法；(c)滑动平均算法；(d) S-G平滑算法

Figure 6. OTDR traces processed by different algorithms. (a) Original traces; (b) cumulative average algorithm; (c) moving average algorithm; (d) S-G smoothing algorithm

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图 7 不同算法归一化阈值定位。(a)原始曲线；(b)累加平均算法；(c)滑动平均算法；(d) S-G平滑算法

Figure 7. Different algorithms for normalized threshold localization. (a) Original trace; (b) cumulative average algorithm; (c) moving average algorithm; (d) S-G smoothing algorithm

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图 8 不同算法的信噪比。(a)原始曲线；(b)累加平均算法；(c)滑动平均算法；(d) S-G平滑算法

Figure 8. Signal to noise ratio of different algorithms. (a) Original trace; (b) cumulative average algorithm; (c) moving average algorithm; (d) S-G smoothing algorithm

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