研究目的
To propose and verify a cross correlation peak-seeking technique based on incomplete Brillouin spectrum for BOTDR to perceive rapid external interference, specifically for monitoring sound barriers along high-speed railways.
研究成果
The cross correlation peak-seeking technique based on IBS splicing is feasible for rapid strain monitoring in BOTDR, with high accuracy and efficiency. Optimal parameters (e.g., 80 IBS and 5 MHz interval achieve 98% accuracy) can be selected based on requirements, extending BOTDR applications to dynamic fields like high-speed railway monitoring. Future work should consider real-world implementations and SNR effects.
研究不足
The method requires splicing multiple IBS, which increases measurement time and may not be feasible if insufficient IBS are available (e.g., limited train passes). Accuracy is affected by the signal-to-noise ratio; lower SNR requires more splicing times. The frequency interval cannot be increased indefinitely without losing information and causing deviations. The study is based on numerical simulations and may need validation with real-world experiments.
1：Experimental Design and Method Selection:
The study uses numerical simulations to verify the cross correlation algorithm for peak frequency detection in BOTDR. The algorithm involves splicing incomplete Brillouin spectra (IBS) and performing cross correlation with a reference Lorentzian curve to estimate the Brillouin frequency shift.
2：Sample Selection and Data Sources:
Simulations are based on a primal Brillouin spectrum obtained from experiments, with parameters such as frequency interval and number of IBS varied. The spectrum is modeled as a Lorentzian curve with added noise.
3：List of Experimental Equipment and Materials:
Not explicitly detailed in the paper; based on BOTDR sensing system components like optical fibers, light sources, and detectors, but no specific models or brands are mentioned.
4：Experimental Procedures and Operational Workflow:
Generate IBS by randomly extracting consecutive frequency points from the primal spectrum. Splice multiple IBS together. Perform cross correlation between the spliced spectrum and a reference curve. Calculate the peak frequency and compare it to the true value. Repeat simulations 50 times for statistical analysis under different conditions (e.g., number of IBS and frequency interval).
5：Data Analysis Methods:
Statistical analysis of peak frequency deviations, probability calculations for accuracy within 0.50 MHz error, and optimization of parameters (splicing times and frequency interval) based on simulation results.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
