基于集成移动窗口的可调谐滤波器温度补偿研究

Temperature Compensation of Tunable Filter Based on Integrated Moving Window

光纤法布里-珀罗可调滤波器(FFP-TF)是组成光纤布拉格光栅(FBG)传感器解调系统的核心器件之一,其稳定性对解调精度的提高至关重要,而温度漂移是影响其稳定性的关键因素之一。针对实际应用中遇到的FFP-TF在变温环境下产生的透射波长漂移问题,提出了一种基于集成移动窗口的温度漂移补偿方法。利用最小二乘支持向量机对透射波长和温度之间的非线性关系进行了建模,并在训练样本中建立了移动窗口。此外,为了挖掘更多的训练样本过程信息,提出了采用神经网络对各个移动窗口的补偿结果进行加权集成的方法。实验结果表明,当未引入集成移动窗口时,解调值的最大测量误差为±13.5 pm。在引入集成移动窗口后,解调值的最大测量误差为±0.82 pm,即所提方法有效提高了光纤光栅传感中可调滤波器解调的温度稳定性。

Fiber Fabry-Perot tunable filter(FFP-TF)is one of the core components of the demodulation system of a fiber Bragg grating(FBG)sensor.Its stability is essential to demodulation accuracy improvement,and temperature drift is one of the key factors affecting its stability.Given the problem of the transmission wavelength drift of FFP-TF in a variable temperature environment,a temperature drift compensation method based on the integrated moving window is proposed in this paper.The nonlinear relationship between transmission wavelength and temperature is modeled by the least squares support vector machine,and a moving window is built in the training samples.Moreover,a weighted integration method of the compensation result of each moving window through the neural network is proposed to mine more process information of the training samples.The experimental results show that the maximum measurement error of demodulation is ±13.5 pm when the integrated moving window is not introduced,and that is ±0.82 pm after introducing the integrated moving window,which means the proposed method effectively improves the temperature stability of the tunable filter demodulation in fiber grating sensing.