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 (Ф-OTDR) is one of the most commonly used DOVS schemes. For Ф-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 Ф-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 Ф-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 Ф-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 Ф-OTDR while maintaining the cost of the system as no additional hardware is required.

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial–temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been proposed for distributed vibration sensing purpose over recent years. Emerging applications, including seismic and hydroacoustic wave detection, demand accurate low-frequency vibration reconstruction capability. We propose to use the direct-detection Φ-OTDR configuration to achieve quantitative demodulation of external low-frequency vibrations by phase-shifted dual-pulse probes. Simultaneous pulsing and phase shifting modulation is realized with a single acousto-optic modulator to generate such probes, relaxing the need for an additional optical phase modulator. In the experiments, vibrations with frequency as low as 0.5 Hz are successfully reconstructed with 10 m spatial resolution and 35 dB signal-to-noise ratio. Excellent linearity and repeatability are demonstrated between the optical phase demodulation results and the applied vibration amplitudes. The proposed method is capable of quantitative demodulation of low-frequency vibrations with a cost-effective system configuration and high computation efficiency, showing potential for commercial applications of distributed seismic or hydroacoustic wave acquisition.

Curvature measurement plays an important role in structural health monitoring, robot-pose measuring, etc. High-resolution curvature measurement is highly demanded. In this paper, an optical curvature sensor with high resolution based on in-fiber Mach-Zehnder interferometer (MZI) and microwave photonic filter (MPF) is proposed and experimentally demonstrated. The in-fiber MZI is constructed with a ring-core fiber (RCF) which is fusion spliced between two coreless fibers (CLFs). The structure of CLF-RCF-CLF is then sandwiched between two segments of single-mode fiber (SMF), making the whole interferometer structure of SMF-CLF-RCF-CLF-SMF. The operating principle is that different curvatures will cause the variations of the interference spectrum of MZI due to elastic-optic effect, and then the variations are converted into the frequency-shift of the MPF. The factors affecting the visibility of the interference spectrum are researched. A preliminary exploration of the multiplexing demodulation for the in-fiber-MZIs is also investigated and discussed, which is for the first time to the best of our knowledge, holding great potential to pave the way for constructing the sensing network composed of interferometric sensors. The curvature measurement sensitivity is -147.634 MHz/m^-1, and the resolution is 6.774 × 10⁻⁶ m^-1 which is the highest value up to now.

A fiber-optic Mach Zehnder interferometer (MZI) refractive index (RI) sensor based on a double peanut-shaped structure in Er-doped fiber with a section of tapered fiber was fabricated using fusion splicer and experimentally demonstrated. The double peanut-shaped structures placed at both ends of the sensor function as beam splitting/combining. Besides, the tapered region works as the sensing area. With RI changes, the optical path difference between the core mode and the cladding modes will change correspondingly, resulting in interference spectrum shifting. By monitoring the shift, the measured RI can be obtained. The experimental results show that the RI sensitivity was significantly improved compared with other types of peanut-shaped fiber sensors. By comparing different taper diameters, it is found that the finer the tapered fiber, the higher the sensitivity of measuring RI.High RI sensitivity of 441.56 nm/RIU is obtained in the experiments. Compared with the similar-structure sensor consisted of single-mode fiber (SMF), the sensitivity of the proposed sensor based on Er-doped fiber is 115.22 nm/RIU higher than that of the former. The proposed sensor has the characteristics of a compact structure. In addition, it has potential in environmental, medical, and aerospace monitoring applications.