光纤布拉格光栅传感解调中的寻峰算法

Peak-Detection Algorithm in the Demodulation for the Fiber Bragg Grating Sensor System

分析比较了光纤布拉格光栅(FBG)传感解调系统的6种寻峰算法:蒙特卡罗(Monte Carlo)算法、直接比较法、二次插值数值微分法、一般多项式拟合法、多项式-高斯公式拟合法和高斯公式非线性曲线拟合法。通过分析算法理论误差,结合算法仿真和实验研究,给出了算法误差及相关影响因素。研究发现输入信号的信噪比与寻峰算法的算法误差呈线性关系,相同条件下,高斯公式非线性曲线拟合法获得的精度最高。在光纤布拉格光栅传感实验系统中,对于信噪比为40dB的输入信号,算法的寻峰精度可达到0.44pm。结果表明,算法输入信噪比是影响寻峰算法中算法误差的决定因素,寻峰算法中的最佳算法为高斯公式非线性曲线拟合法。

This paper is focused on the peak-detection algorithms in the demodulation for the fiber Bragg grating （FBG） sensor system. 6 peak-detection algorithms have been analyzed and compared, such as the Monte-Carlo algorithm, the direct peak-located algorithm and the quadratic polynomial numerical derivative algorithm, the polynomial fitting, the polynomial-Gaussian fitting and the Gaussian nonlinear curve fitting. The theoretical and practical errors and the relative effect factors of errors were introduced, analyzed and evaluated by the combination of the simulations and the experiments. It is demonstrated that the relationship between the signal noise ratio （SNR） at the input of the algorithms and the error is linear. When the SNR is constant, the error in using Gaussian nonlinear curve fitting is the lowest. The error can be only 0.44 pm when the SNR is 40 dB in the FBG sensor experiment. Consequently SNR is the major factor which dominates the errors of the peak-detection algorithms in the demodulation and the Gaussian nonlinear curve fitting is considered as the best peak-detection algorithm.