Brillouin Frequency Shift Extraction Based on AdaBoost Algorithm

The Brillouin Optical Time-Domain Analyzer assisted by the AdaBoost Algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 9; p. 3354
Main Authors: Zheng, Huan, Xiao, Feng, Sun, Shijie, Qin, Yali
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 27.04.2022
MDPI
Subjects:
AdaBoost algorithm
Algorithms
Brillouin frequency shift
Brillouin scattering
Classification
Communication
Decision trees
distributed fiber sensing
Neural networks
Noise
Simulation
Standard deviation
Support vector machines
Brillouin frequency shift
distributed fiber sensing
Brillouin scattering
AdaBoost algorithm
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The Brillouin Optical Time-Domain Analyzer assisted by the AdaBoost Algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the AdaBoost Algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22093354