Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm
Distributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phase-sensitive optical time-domain reflecto...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/5720695 |
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| author | Bernard Marie Tabi Fouda Dezhi Han Bowen An Xiangzhi Chen |
| author_facet | Bernard Marie Tabi Fouda Dezhi Han Bowen An Xiangzhi Chen |
| author_sort | Bernard Marie Tabi Fouda |
| collection | DOAJ |
| description | Distributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is used to detect the signals generated during events (intrusions or nonintrusion). This paper proposes the time-frequency characteristic (TFC) method for the recognition of the fiber vibration signal and designs and implements the corresponding software function module. The combination of time-domain features and time-frequency-domain features is called TFC; and it is based on the Hilbert transform and on the empirical mode decomposition (EMD) of time-frequency entropy and center-of-gravity frequency that is described. A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. The experimental simulation results show that the monitoring system software can intelligently display the data collected in real time, which demonstrates that the proposed method is effective and reliable in identifying and classifying accurately the types of events. The data processing time is less than 2 s, and the accuracy of the system identification can reach 99%, which ensures the system’s validity. |
| format | Article |
| id | doaj-art-84090e482cc549ef9046587c7e46dce3 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-84090e482cc549ef9046587c7e46dce32025-02-03T06:46:52ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552020-01-01202010.1155/2020/57206955720695Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition AlgorithmBernard Marie Tabi Fouda0Dezhi Han1Bowen An2Xiangzhi Chen3Department of Information Engineering, Shanghai Maritime University, Haigang Avenue 1550, Shanghai 201306, ChinaDepartment of Information Engineering, Shanghai Maritime University, Haigang Avenue 1550, Shanghai 201306, ChinaDepartment of Information Engineering, Shanghai Maritime University, Haigang Avenue 1550, Shanghai 201306, ChinaIndependent Researcher, Wuxi, ChinaDistributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is used to detect the signals generated during events (intrusions or nonintrusion). This paper proposes the time-frequency characteristic (TFC) method for the recognition of the fiber vibration signal and designs and implements the corresponding software function module. The combination of time-domain features and time-frequency-domain features is called TFC; and it is based on the Hilbert transform and on the empirical mode decomposition (EMD) of time-frequency entropy and center-of-gravity frequency that is described. A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. The experimental simulation results show that the monitoring system software can intelligently display the data collected in real time, which demonstrates that the proposed method is effective and reliable in identifying and classifying accurately the types of events. The data processing time is less than 2 s, and the accuracy of the system identification can reach 99%, which ensures the system’s validity.http://dx.doi.org/10.1155/2020/5720695 |
| spellingShingle | Bernard Marie Tabi Fouda Dezhi Han Bowen An Xiangzhi Chen Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm Journal of Electrical and Computer Engineering |
| title | Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm |
| title_full | Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm |
| title_fullStr | Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm |
| title_full_unstemmed | Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm |
| title_short | Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm |
| title_sort | research and software design of an φ otdr based optical fiber vibration recognition algorithm |
| url | http://dx.doi.org/10.1155/2020/5720695 |
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