Zhao Zhi-Yuan, Zeng Jie, Liang Da-Kai, Zhang Xiao-Li
The Aeronautical Science Key Laboratory for Smart Material and Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Nov;29(11):3096-100.
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.
根据表面等离子体共振（SPR）光纤传感器光谱的特点以及实时监测的要求，本文提出了一种新的噪声滤波方法——移动提升小波分析。该方法并非基于傅里叶变换，而是基于一种将复杂的噪声滤波过程分解为多个可逆简单过程的算法。它速度非常快、准确且不使用额外内存。通过在理论光谱上叠加一定强度的高斯白噪声构建了一个实际的SPR光纤传感器光谱模型，其共振波长为557.70nm。分别基于Haar、CDF(3,1)、DD(4,2)和5/3母小波，运用移动提升小波分析对模拟光谱进行噪声滤波，并再次计算共振波长。对应列出的每种方法，结果分别为556.45、564.06、557.27和557.91nm。因此，基于5/3母小波的新方法在噪声滤波后得到的相对误差为0.0377%，是最小的。它也低于传统symlet11小波分析在噪声滤波后得到的0.4303%，传统symlet11小波分析已被证明对SPR光纤传感器光谱有效。在不同时间采集了一个SPR光纤传感器检测系统的多个光谱，并基于5/3母小波进行了移动提升小波分析。结果表明，共振波长的标准差从噪声滤波前计算的4.1867nm降至1.5608nm。不出所料，该结果优于使用传统symlet11小波分析进行相同实验的结果，传统symlet11小波分析仅将标准差从4.1867nm降至2.7253nm。研究表明，移动提升小波分析显著抑制了系统噪声，减少了噪声对从SPR光纤传感器光谱中采集共振波长信息的影响，并为SPR光纤传感器的实际准确检测提供了保障。
