The key technology for expanding the scope of application fields for the phase-sensitive optical time domain based reflectometer (Φ-OTDR) is to accurately demodulate the vibration position with a wide frequency range. Unfortunately, due to the restriction of the traveling time of the modulated pulse, it is very difficult to obtain the low frequency response with a high signal to noise ratio (SNR) and to achieve a higher detectable frequency response in the Φ-OTDR. In this paper, a scheme based on dual-wavelength probes is proposed and demonstrated to enhance the dynamic frequency range with a high SNR in the Φ-OTDR based distributed optical fiber vibration sensing system. Utilizing the small difference between the central wavelengths of the two laser sources, the location and the frequency information can be simultaneously obtained by the reflectometer and a symmetric based interferometer. In addition, to further improve the SNR of the demodulated vibration spectrum and to suppress the high-order harmonic effect induced by the piezoelectric transducer, an enhanced phase generated carrier demodulation algorithm is employed in the proposed scheme. In experiments, the proposed sensing scheme showed better performance compared to previous studies. Foreseeably, the proposed sensing scheme will greatly extend the sensing scope for the Φ-OTDR based system where a wide frequency response is required.
In this paper, we propose an improved denoising and positioning method for fiber optic perimeter security system based on the φ-OTDR. We combine the lifting scheme of the discrete wavelet transform (LSDWT) and the total variation (TV) based image denoising algorithm to suppress the background noises and magnify the man-made intrusion signals, and the positions of man-made intrusion can be acquired with the denoised signals through the variance-based positioning method, which mainly determines the man-made intrusion signals by calculating the variance of the temporal signal. A series of experiments have been carried out to validate this method. In the scheme, three types of man-made intrusion behaviors such as knocking, waggling, and tapping were implemented at the end of the 40 km optical cable, while the background noises such as natural noise and sound of car driving were continuing. Compared with the original signals, the peak signal-to-noise ratio (PSNR) of the signals after the proposed scheme has been greatly enhanced, and a ±15m positioning error can be realized at the end of the 40 km fiber cable. Therefore, the proposed method can provide new thinking to address the high positioning error at the end of the long-distance optical fiber cable.
KEYWORDS: Signal to noise ratio, Signal detection, Sensing systems, Signal processing, Mach-Zehnder interferometers, Detection and tracking algorithms, Sensors, Optical fibers
In this paper, we propose a fast endpoint detection method for dual Mach-Zehnder interferometer-based vibration sensing system. Firstly, we analyze the varying trend of the signal by derivation to detect the approximate endpoint. Secondly, we split part of the signal before the estimated endpoint into frames. Finally, we estimate the signal to noise ratio of each frame to calibrate the endpoint. Experiments have been carried out to verify the effectiveness of the proposed algorithm. The results show that the accuracy is improved by using threshold judgement twice. And the mean processing time of the proposed method is 0.024s which is at least three times faster than the conventional zero-crossing ratio-based method which is the fastest algorithm as we know. Therefore, the proposed method has great potential in real-time monitoring based on distributed fiber interferometer vibration system.
A multi-dimensional hybrid feature extraction scheme for optical fiber distributed vibration sensing system has been proposed in this work. Firstly, the hybrid features are extracted through using zero-crossing rate, wavelet packet energy entropy, empirical mode decomposition based kurtosis, skewness and multiscale permutation entropy. Secondly, an effective feature vector are built based on all the above-mentioned features. Finally, the established features are classified and recognized by a radial basis foundation neural network. A series of experiments have been implemented to validate the effectiveness of the proposed scheme. Results show that the proposed scheme can efficiently and accurately identify five common sensing patterns. Specifically, the average recognition accuracy of 99.2 % is achieved and the response time can be limited less than 1 s. Therefore, it is believed that the proposed pattern recognition scheme has a good potential in the application of the optical fiber distributed vibration sensing system.
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