Share Email Print

Proceedings Paper

Pattern recognition of fiber disturbance based on support vector machine in polarization optical time domain reflectometry
Author(s): Menghao Li; Jing Gu; Xiaosong Luo; Bohang Xiong; Jingzeng Li; Feng Wang; Rongrong Dou
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Aiming at improving the signal processing capability of polarization optical time domain reflectometry (POTDR), a recognition method mainly based on the feature extraction and supported vector machine (SVM) is proposed. Apart from locating the certain place of interruptions, this method can help us identify different kinds of intrusion events. Firstly, we preprocess the signal by using an average filter and setting a proper threshold for it. Secondly, the signal is transformed into various kinds of time-domain features and frequency-domain features for the subsequent classification. Finally, the SVM of the system is trained with initial signals so it can discriminate events represented by new signal accurately. Our experiment results show the effectiveness of this method, and it can work well with high accuracy, fast response speed and low cost.

Paper Details

Date Published: 12 March 2020
PDF: 9 pages
Proc. SPIE 11436, 2019 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, 114360Y (12 March 2020); doi: 10.1117/12.2551736
Show Author Affiliations
Menghao Li, Nanjing Univ. (China)
Jing Gu, Nanjing Univ. (China)
Xiaosong Luo, Nanjing Univ. (China)
Bohang Xiong, Nanjing Univ. (China)
Jingzeng Li, Jiangsu Southeast Engineering Consulting Co., Ltd. (China)
Feng Wang, Nanjing Univ. (China)
Rongrong Dou, Nanjing Univ. (China)

Published in SPIE Proceedings Vol. 11436:
2019 International Conference on Optical Instruments and Technology: Optical Sensors and Applications
Xuping Zhang; Hai Xiao, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?