Improvement of signal-to-noise ratio for phase-sensitive optical time-domain reflecting system
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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。
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关键词:
- 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.
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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
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
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
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
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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