研究目的
To propose an algorithm for selecting an optimized sliding-window length in distributed optical fiber strain measurement based on a theoretical model that estimates strain measurement error using noise variance and quality factor of the Rayleigh backscattering spectrum.
研究成果
The theoretical model and proposed algorithm for selecting sliding-window length are validated through experiments, showing good agreement. The method improves strain measurement reliability and accuracy by automating window length selection based on quality factor and noise variance, reducing user input requirements. Future work should focus on adaptive sliding-window approaches for enhanced accuracy.
研究不足
The theoretical model assumes the signal is much larger than the noise, which may not hold in all practical scenarios. The quality factor Q is evaluated for the overall RBS, but adaptive sliding-window lengths based on individual subset quality could further improve accuracy, which is not addressed here.
1:Experimental Design and Method Selection:
The study involves establishing a theoretical model for strain measurement error and proposing an algorithm for sliding-window optimization. Experiments include self-correlation and virtual noise influence experiments to verify the model.
2:Sample Selection and Data Sources:
Three sets of Rayleigh backscattering spectrum (RBS) signals (A, B, C) with different wavelength ranges are used, acquired from an optical-frequency-domain reflectometer (OFDR) system.
3:List of Experimental Equipment and Materials:
OFDR system consisting of a tunable laser (TLS), couplers (C1-C5), photodetector (PD), balanced photodetector (BPD), digital acquisition card (DAQ), and fiber under test (FUT). PI piezo nanometer for inducing strains.
4:Experimental Procedures and Operational Workflow:
RBS data is recorded using OFDR, transformed via FFT and IFFT, subsets are generated with sliding-windows, cross-correlation analysis is performed to obtain strain, and noise is simulated for virtual experiments.
5:Data Analysis Methods:
Statistical analysis of strain calculation errors, comparison with theoretical predictions, and calculation of quality factor Q using defined equations.
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