减少统计小群体采样对 FBG 光学传感阈值检测的影响

G. Cibira, Ivan Glesk, Jozef Dubovan, Daniel Benedikovič
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摘要

人们研究了许多从共享媒体(如承载不同类型通信、传感和数据流的光纤)中恢复信息的技术。本文重点介绍一种简单的方法,在使用统计群体采样时,以最少的必要重要样本数检索目标信息。这里的重点是光纤布拉格光栅(FBG)功率谱的统计去噪和检测。研究了双侧和单侧滑动窗口技术的影响。滑动窗口的大小最多可达对称 FBG 功率谱带宽的二分之一。实验研究了双侧和单侧小群体采样技术。我们发现,滑动窗口越短,处理延迟越短,这将有利于实时应用。计算出的检测阈值被用于深入分析我们获得的数据。结果发现,在使用小群体采样时,不需要遵循正态三Σ规则。实验演示和分析还表明,新型去噪和统计阈值检测并不依赖于描述 FBG 功率谱峰值和背景噪声的概率分布函数的先验知识。我们已经证明,检测阈值的适应性在很大程度上取决于小群体采样的平均值和标准偏差值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing
Many techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of significant samples when using statistical population sampling. Here, the focus is on the statistical denoising and detection of the fiber Bragg grating (FBG) power spectra. The impact of the two-sided and one-sided sliding window technique is investigated. The size of the window is varied up to one-half of the symmetrical FBG power spectra bandwidth. Both, two- and one-sided small population sampling techniques were experimentally investigated. We found that the shorter sliding window delivered less processing latency, which would benefit real-time applications. The calculated detection thresholds were used for in-depth analysis of the data we obtained. It was found that the normality three-sigma rule does not need to be followed when a small population sampling is used. Experimental demonstrations and analyses also showed that novel denoising and statistical threshold detection do not depend on prior knowledge of the probability distribution functions that describe the FBG power spectra peaks and background noise. We have demonstrated that the detection thresholds’ adaptability strongly depends on the mean and standard deviation values of the small population sampling.
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