Noise reduction method based on timed frequency hopping in long distance optical fiber sensing system

Liang, Yun; Lidong, Lv; Huang, Li; Wang, Dayang; Li, Pei

doi:10.3233/jcm-180793

Cited by 4 publications

(1 citation statement)

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“…Since the structure of self-noise is similar to random noise, and median filter is an effective method to remove random noise, Qin et al [23] moved the noise from signals in three-dimensional time domain with median filter. In order to eliminate the influence of coherent Rayleigh noise in coherent optical time domain reflectometry (COTDR) systems, Liang et al [24] proposed a noise reduction method based on timed frequency hopping. This method could effectively suppress the noise in COTDR system and improve the long-distance sensing performance of the system.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

Feng

Zhang

et al. 2019

Sensors

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Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.

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Section: Related Workmentioning

confidence: 99%