Detection and recognition of distributed optical fiber intrusion signal

Zhang Yongkang; Shang Ying; Wang Chen; Zhao Wen′an; Li Chang; Cao Bing; Wang Chang

doi:10.12086/oee.2021.200254

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Zhang Y K, Shang Y, Wang C, et al. Detection and recognition of distributed optical fiber intrusion signal[J]. Opto-Electron Eng, 2021, 48(3): 200254. doi: 10.12086/oee.2021.200254

Citation:

Zhang Y K, Shang Y, Wang C, et al. Detection and recognition of distributed optical fiber intrusion signal[J]. Opto-Electron Eng, 2021, 48(3): 200254. doi: 10.12086/oee.2021.200254

Detection and recognition of distributed optical fiber intrusion signal

Laser Institute of Shandong Academy of Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China

Fund Project: Natural Science Foundation of Shandong Province (ZR2019QF011), Science and Technology Innovation Project of Shandong Province - Major Special (2019JZZY010113), Key R & D Program of Shandong Province (2019GSF111065), and the Youth Innovation Science and Technology Program of Colleges in Shandong Province (2019KJJ004)

More Information

Corresponding author: Wang Chang, E-mail: ch_wangs@163.com

Received Date 10 July 2020

Revised Date 20 November 2020

Published Date 15 March 2021

Abstract

Abstract

Distributed acoustic sensing (DAS) technology can detect acoustic or vibration signals with high sensitivity and wide dynamic range by receiving the phase information from coherent Rayleigh scattered light. Linear quantization is used to measure high fidelity restoration of the signals. With the increasing demand of practical applications, the optical fiber intrusion detection field has put forward higher requirements for event location and identification, which is manifested as the accurate classification of intrusion events. Therefore, the combination of distributed acoustic sensing and pattern recognition (PR) technology is a hot research topic at present. This is beneficial to promote the application and development of distributed optical fiber sensing technology. The research progress of the pattern recognition technology applied to distributed optical fiber intrusion detection in recent years is summarized in this paper, which can be used for feature extraction and classification algorithm research progress. In this paper, several feature extraction methods for realizing intrusion event signal recognition and their feature selection difficulties in different application situations are reviewed. Meanwhile, the advantages and disadvantages of specific event recognition algorithm are analyzed and summarized.
- distributed acoustic sensing /
- pattern recognition /
- feature extraction /
- classification algorithms

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References

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Overview

Overview

Overview: Developed on the basis of phase-sensitive optical time domain reflectrometer(Φ-OTDR), distributed acoustic sensing(DAS) is a new type of distributed optical fiber sensing technology. It is a hot topic that how to accurately distinguish the type of intrusion events from complex signals, which attachs greater importance to the research on the pattern recognition technology based on DAS.

Intrusion signal recognition mainly includes two parts, feature extraction and classification algorithm. This paper summarizes the currently widely used feature extraction methods. Generally speaking, the time-domain features are simple, intuitive, and fast in response, but they are susceptible to noise. The frequency-domain features can obtain the inherent spectrum characteristics of the signal, but cannot reflect the frequency changes of the signal at every moment. The time-frequency domain features can express the time-domain and frequency-domain information of the signal, and the extracted feature information is also more accurate.

Classification algorithms include two categories, unsupervised learning and supervised learning. Supervised learning need to collect a large amount of data for training and verification. Therefore, supervised learning algorithms are mostly used in the fiber intrusion detection applications. Support vector machines (SVM) and BP neural networks are relatively common models for classification. In recent years, with the development of deep learning technology, building deep neural network models is very helpful for classification recognition. Therefore, models such as convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN) are used in the field of distributed optical fiber intrusion signal recognition, which have achieved great performance.

In summary, choosing the proper feature extraction method and classification algorithm will greatly enhance the accuracy of intrusion signal recognition. Facing the increasing actual demands, the pattern recognition technology based on the DAS system will definitely play a more important role and fulfill its potential in the future.