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Zhang B Z, Liu K, Liu K, et al. Research on dynamic variance threshold algorithm based on distributed fiber vibration sensor system[J]. Opto-Electron Eng, 2023, 50(2): 220205. doi: 10.12086/oee.2023.220205
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Research on dynamic variance threshold algorithm based on distributed fiber vibration sensor system
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Abstract
In order to solve the problems of weak positioning accuracy, low sensitivity, and slow response speed of the distributed fiber vibration sensor system, a dynamic variance threshold algorithm based on the phase-sensitive photosensitive time domain reflection is proposed. The signal preprocessed by the band-pass filter is processed by variance processing, Gaussian blur, threshold peak seeking, and accurate center of gravity. The problem of long response time caused by the attenuation of Rayleigh scattering signal and the large amount of computation in the long-distance DVS detection is solved. The parallel programming technology is used to improve the operation speed by 184%, so as to quickly and accurately determine the location of the disturbance. The difference between the man-made disturbance and the noise on a 39 km long optical fiber is experimentally studied, and the influence of the noise is eliminated by the dynamic variance algorithm. The response time of the system is 1 second, the spatial resolution is 20 meters, and the positioning error is less than 0.1%.-
Keywords:
- ϕ-OTDR /
- variance /
- gaussian blur /
- threshold value
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References
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Zhang B Z, Liu K, Liu K, et al. Research on dynamic variance threshold algorithm based on distributed fiber vibration sensor system[J]. Opto-Electron Eng, 2023, 50(2): 220205. doi: 10.12086/oee.2023.220205
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Figure 1.
Algorithm flow chart
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Figure 2.
Structural diagram
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Figure 3.
Signal characteristics of two quiet positions and their corresponding positions in the spectrum (abscissa is position/m, ordinate is frequency/Hz)
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Figure 4.
The data of 1000 frames at the disturbance location and the corresponding position of its spectrum (abscissa is position/m, ordinate is frequency/Hz)
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Figure 5.
Gaussian blur. (a) Before blurring; (b) Average blurring with a width of 20; (c) Gaussian fuzzy. The standard deviation sigma of Gaussian fuzzy is equal to 3, and the width is 21 points
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Figure 6.
Test site
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Figure 7.
Raw data
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Figure 8.
Variance treatment
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Figure 9.
Disturbance prescription difference curve
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Figure 10.
Peak seeking algorithm
