根据光纤SPR传感器光谱的特点和实验系统的实时性监测要求, 提出了一种新的降噪方法——移动小波提升算法。 该方法脱离傅里叶变换, 将复杂滤波过程分解为若干可逆简单过程, 处理快而准, 无须额外内存。 在共振波长为557.70 nm的理论光谱上加一定强度的高斯白噪声, 得到了仿真光谱模型。 分别用基于Haar, CDF(3, 1), DD(4, 2)和5/3小波的移动小波提升算法对此模型进行降噪, 得到共振波长分别为556.45, 564.06, 557.27和557.91 nm。 最后一种算法相对误差最小, 仅0.037 7%。 这比用经证实降噪效果较好的symlet11小波降噪法得到的0.043 03%更低。 不同时刻采集同一检测系统的多组光谱, 其共振波长标准偏差为4.186 7 nm, 经新算法降噪后降为1.560 8 nm, 也优于symlet11小波降噪法的2.725 3 nm。 研究表明, 用移动小波提升算法降噪有效抑制了系统噪声, 减小了噪声对检测共振波长的影响, 保证了光纤SPR传感器的检测精度。

According to the characters of SPR optical fiber sensor spectrum and the requirement of real-time monitoring, a new noise filtering method, mobile lifting wavelet analysis, is presented in this paper. This method is not based on Fourier transform, but on an algorithm of dividing a complicated noise filtering process into a number of reversible simple processes. It is very fast, correct and does not use additional memory. A model of actual SPR optical fiber sensor spectrum is constructed by superposing a certain intensity of Gaussian white noise on the theoretical spectrum, of which the resonant wavelength is 557.70 nm. It applies noise filtering to the simulative spectrum with mobile lifting wavelet analysis based on Haar, CDF(3,1), DD(4,2) and 5/3 mother wavelet respectively and calculates the resonant wavelengths again. The results are 556.45, 564.06, 557.27 and 557.91 nm corresponding to each method listed. So a relative error of 0.037 7 percent, obtained after noise filtering with new method based on 5/3 mother wavelet, is the minimum one. It is also lower than 0.430 3 percent obtained after noise filtering with traditional symlet11 wavelet analysis that has been proved to be effective for SPR optical fiber sensor spectrum. At different time gather several spectra of one SPR optical fiber sensor detective system were gathered and mobile lifting wavelet analysis based on 5/3 mother wavelet was done. The result shows that, the standard deviation of resonant wavelengths is reduced to 1.560 8 from 4.186 7 nm, which is calculated before noise filtering. As expected, this result is better than doing the same experiment with traditional symlet11 wavelet analysis, which only reduces the standard deviation to 2.725 3 from 4.186 7 nm. The research shows that mobile lifting wavelet analysis significantly suppresses the system noises, reduces noise influence on the gathering of resonant wavelength information from SPR optical fiber sensor spectrum and gives a guarantee to actual accurate detection with SPR optical fiber sensor.