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JOURNAL OF MECHANICAL ENGINEERING  > 2021  >  48(3): 200254.
Zhang Yongkang, Shang Ying, Wang Chen, Zhao Wen′an, Li Chang, Cao Bing, Wang Chang. Detection and recognition of distributed optical fiber intrusion signal[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 48(3): 200254. doi: 10.12086/oee.2021.200254
Citation: Zhang Yongkang, Shang Ying, Wang Chen, Zhao Wen′an, Li Chang, Cao Bing, Wang Chang. Detection and recognition of distributed optical fiber intrusion signal[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 48(3): 200254. doi: 10.12086/oee.2021.200254
shu

Detection and recognition of distributed optical fiber intrusion signal

doi: 10.12086/oee.2021.200254
Funds:

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

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 Jul 2020
  • Rev Recd Date: 20 Nov 2020
    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.

     

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