Abstract
Distributed optical vibration sensors (DOVS) have attracted much attention recently since it can be used to monitor mechanical vibrations or acoustic waves with long reach and high sensitivity. Phase-sensitive optical time domain reflectometry (Φ-OTDR) is one of the most commonly used DOVS schemes. For Φ-OTDR, the whole length of fiber under test (FUT) works as the sensing instrument and continuously generates sensing data during measurement. Researchers have made great efforts to try to extract external intrusions from the redundant data. High signal-to-noise ratio (SNR) is necessary in order to accurately locate and identify external intrusions in Φ-OTDR systems. Improvement in SNR is normally limited by the properties of light source, photodetector and FUT. But this limitation can also be overcome by post-processing of the received optical signals. In this context, detailed methodologies of SNR enhancement post-processing algorithms in Φ-OTDR systems have been described in this paper. Furthermore, after successfully locating the external vibrations, it is also important to identify the types of source of the vibrations. Pattern classification is a powerful tool in recognizing the intrusion types from the vibration signals in practical applications. Recent reports of Φ-OTDR systems employed with pattern classification algorithms are subsequently reviewed and discussed. This thorough review will provide a design pathway for improving the performance of Φ-OTDR while maintaining the cost of the system as no additional hardware is required.
Highlights
O VER the past few decades, distributed fiber optical sensors (DOFS) have been intensively studied owing to the large-scale monitoring range, fully distributed manner, accurate localization and low cost [1]–[4]
Pattern classification algorithms can automatically identify different types of external intrusion events according to the signal characteristics extracted from the raw data, which have been widely applied in many fields such as automated driving, handwriting recognition and biomedical applications
More advanced features and modified classification algorithms can be explored for recognition accuracy improvement of -OTDR systems
Summary
O VER the past few decades, distributed fiber optical sensors (DOFS) have been intensively studied owing to the large-scale monitoring range, fully distributed manner, accurate localization and low cost [1]–[4]. Pattern classification algorithms can automatically identify different types of external intrusion events according to the signal characteristics extracted from the raw data, which have been widely applied in many fields such as automated driving, handwriting recognition and biomedical applications. With a hybrid of SNR enhancement and pattern classification algorithms, intrusion signals can be demodulated from the redundant raw data of DOVS more accurately. This advanced version of data-driven DOVS system is suitable for sophisticated applications, such as border breach monitoring, pipeline leakage detection etc
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