Feature Extraction of Brillouin Scattering Spectrum Based on Cross-Correlation Convolution and High-Order Centroid Calculation

Qiufeng, 尚秋峰 Shang; Yuting, 胡雨婷 Hu; Wei, 刘薇 Liu

doi:10.3788/cjl201744.1106011

Cited by 3 publications

(1 citation statement)

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“…Farahani et al proposed a Brillouin frequency shift extraction method based on cross-correlation convolution, which improves the signal-to-noise ratio. However, its accuracy is constrained by the degree to which the frequency sweep data adheres to the Lorentz distribution [8]. W. Kang et al optimized the generalized neural network using the differential evolution algorithm, resulting in reduced complexity for manual testing and better fitting for various signal-to-noise ratios and line widths.…”

Section: Introductionmentioning

confidence: 99%

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

Duan,

Liu,

et al. 2024

IEEE Photonics J.

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Distributed Optical Fiber Sensing Technology, with advantages such as long detection distance, resistance to electromagnetic interference, and easy maintenance, is widely applied in various fields including security monitoring, electrical safety, and aerospace. However, the current Distributed Optical Fiber Sensing Technology faces challenges such as short localization distance, limited range for stress measurement, low accuracy, and time-consuming processes. This study proposes a novel approach for extracting Brillouin frequency shift signals using an adaptive gradient descent algorithm (Adam Algorithm). A Brillouin strain testing system based on heterodyne coherent detection is also constructed. Experimental results show that the distributed strain testing system using the Adam Algorithm can achieve accurate and fast measurement of maximum strain up to 9500με within a range of 10 kilometers. The average strain measurement deviation is 32.88με, and the time required for frequency shift extraction is less than 18.5ms. This method provides a theoretical and experimental basis for the application of BOTDR Distributed Optical Fiber Sensing Technology.

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Section: Introductionmentioning

confidence: 99%

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

Duan,

Liu,

et al. 2024

IEEE Photonics J.

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BFS Extraction for Double-Peak Brillouin Gain Curve Based on Triangular Cross Correlation

Wang,

Peng,

Zhou

et al. 2024

IEEE Sensors J.

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Extracting Brillouin frequency shift accurately based on particle swarm optimization and a cross-correlation method

et al. 2022

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A fitting algorithm based on particle swarm optimization (PSO) and the cross-correlation method (XCM) was proposed to accurately extract features of the Brillouin scattering spectrum. Both the simulation and experiment were conducted to verify the validity of the proposed algorithm. The results show that the algorithm achieves high demodulation accuracy and is suitable for Brillouin scattering spectra with different signal-to-noise ratios, full width at half-maximums, frequency sweep intervals, and spectral symmetries. Compared with the Lorenz curve fitting and XCM algorithm, the extraction error of the PSO-XCM can be optimized up to 99.98% and 99.93%, and the fitting degree can be improved by 98%. Moreover, based on the proposed algorithm, the minimum temperature measurement error can reach 0.06°C and the Brillouin frequency shift measurement error is 0.07 MHz in 10 km sensing fiber for the Brillouin optical time domain analysis (BOTDA) sensing system.

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Feature Extraction of Brillouin Scattering Spectrum Based on Cross-Correlation Convolution and High-Order Centroid Calculation

Cited by 3 publications

References 0 publications

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

BFS Extraction for Double-Peak Brillouin Gain Curve Based on Triangular Cross Correlation

Extracting Brillouin frequency shift accurately based on particle swarm optimization and a cross-correlation method

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