Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76178
Title: Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition
Authors: Azad, AK 
Khan, FN 
Alarashi, WH
Guo, N 
Lau, APT 
Lu, C 
Issue Date: 2017
Publisher: Optical Society of America
Source: Optics express, 2017, v. 25, no. 14, p. 16534-16549 How to cite?
Journal: Optics express 
Abstract: We propose and experimentally demonstrate the use of principal component analysis (PCA) based pattern recognition to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the fiber under test (FUT) obtained by Brillouin optical time domain analysis (BOTDA) system. The proposed scheme employs a reference database consisting of relevant ideal BGSs with known temperature attributes. PCA is then applied to the BGSs in the reference database as well as to the measured BGSs so as to reduce their size by extracting their most significant features. Now, for each feature vector of the measured BGS, we determine its best match in the reference database comprised of numerous reduced-size feature vectors of the ideal BGSs. The known temperature attribute corresponding to the best-matched BGS in the reference database is then taken as the extracted temperature of the measured BGS. We analyzed the performance of PCA-based pattern recognition algorithm in detail and compared it with that of curve fitting method. The experimental results validate that the proposed technique can provide better accuracy, faster processing speed and larger noise tolerance for the measured BGSs. Therefore, the proposed PCA-based pattern recognition algorithm can be considered as an attractive method for extracting temperature distributions along the fiber in BOTDA sensors.
URI: http://hdl.handle.net/10397/76178
ISSN: 1094-4087
EISSN: 1094-4087
DOI: 10.1364/OE.25.016531
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