光时域反射计
小波
选择(遗传算法)
信号(编程语言)
分解
计算机科学
图层(电子)
小波变换
信号处理
光学
人工智能
模式识别(心理学)
电信
材料科学
物理
光纤
纳米技术
光纤传感器
生物
渐变折射率纤维
生态学
雷达
程序设计语言
作者
Yunfei Chen,Kaimin Yu,Minfeng Wu,Lei Feng,Yuanfang Zhang,Peibin Zhu,Wen Chen,Jianzhong Hao
出处
期刊:Photonics
[Multidisciplinary Digital Publishing Institute]
日期:2024-01-31
卷期号:11 (2): 137-137
被引量:5
标识
DOI:10.3390/photonics11020137
摘要
The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry (φ-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured φ-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.
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