Yatseev Vasily A, Butov Oleg V, Pnev Alexey B
Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Science, 125009 Moscow, Russia.
Scientific Educational Centre "Photonics and IR Engineering", Bauman Moscow State Technical University, 105005 Moscow, Russia.
Sensors (Basel). 2024 Mar 4;24(5):1656. doi: 10.3390/s24051656.
This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source's wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers.
本文致力于研究相敏反射测量系统的计量特性，特别关注解决沿光纤响应的不均匀性问题。作者强调了光纤系统中瑞利反射器随机分布带来的挑战，并提出了一种使用互相关算法和机器学习方法评估响应不均匀性的方法，以啁啾反射测量法为例。实验过程包括通过改变光源波长来模拟变形影响，并利用啁啾反射仪估计响应不均匀性。本文还比较了互相关算法和基于神经网络的算法所得结果，结果表明，在测量相位差时，后者的精度提高了34%以上。总之，该研究展示了这种方法如何有效地评估沿光纤不同部分的响应不均匀性。
