三点寻峰算法处理光纤布拉格光栅传感信号

陈勇1; 王坤1; 刘焕淋2; 陈丽娟1; 杨雪1

doi:10.3788/OPE.20132111.2751

您当前的位置：

首页 >

文章列表页 >

三点寻峰算法处理光纤布拉格光栅传感信号

现代应用光学 | 更新时间：2020-08-12

- 三点寻峰算法处理光纤布拉格光栅传感信号
- Processing FBG sensing signal with three-point peak-detection algorithm
- 光学精密工程 2013年21卷第11期页码：2751-2756
- 作者机构：
  
  1. 重庆邮电大学工业物联网与网络化控制教育部重点实验室重庆,400065
  2. 重庆邮电大学光纤通信技术重点实验室重庆,400065
- 作者简介：
- 基金信息：
  
  基于网络编码的光层组播最大吞吐量理论与方法研究()
- DOI：10.3788/OPE.20132111.2751
  中图分类号： TN253;TN929.11
- 收稿日期：2013-04-28，
  
  修回日期：2013-06-02，
  
  网络出版日期：2013-11-22，
  
  纸质出版日期：2013-11-15
- 稿件说明：
移动端阅览
陈勇, 王坤, 刘焕淋, 陈丽娟, 杨雪. 三点寻峰算法处理光纤布拉格光栅传感信号[J]. 光学精密工程, 2013,21(11): 2751-2756

CHEN Yong WANG Kun LIU Huan-lin CHEN Li-juan YANG Xue. Processing FBG sensing signal with three-point peak-detection algorithm[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2751-2756
陈勇, 王坤, 刘焕淋, 陈丽娟, 杨雪. 三点寻峰算法处理光纤布拉格光栅传感信号[J]. 光学精密工程, 2013,21(11): 2751-2756 DOI： 10.3788/OPE.20132111.2751.

CHEN Yong WANG Kun LIU Huan-lin CHEN Li-juan YANG Xue. Processing FBG sensing signal with three-point peak-detection algorithm[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2751-2756 DOI： 10.3788/OPE.20132111.2751.

摘要

为了精确获得光纤布拉格光栅（FBG）传感系统的中心波长，提出了一种新的峰值检测算法，即三点寻峰法。研究了该算法的流程，在此基础上确定了影响峰值检测精度的三要素，即窗口大小、稀疏数据个数和波长间隔。考虑窗口大小的合理选取是提高寻峰精度的关键，本文采用求导法来确定光谱处理窗口大小。然后，设置采样间隔对窗口内光谱进行重采样，获得稀疏光谱数据并进行多项式拟合。最后，根据拟合结果选取合适的波长间隔，从而确定三点坐标并对FBG反射谱进行峰值检测。仿真实验结果表明：当光谱处理窗口大小为0.4 nm、重采样间隔为0.18 nm时获得了3个光谱数据；其波长间隔取0.22 nm时，所提方法的峰值误差为0.4 pm，好于其他寻峰算法，满足精确解调FBG传感系统的要求。

Abstract

To obtain the center wavelength of a Fiber Bragg Grating(FBG) sensing system

an improved peak detecting method

three-point peak-detection，was proposed. After researching on the process of algorithm

three elements(window sizes

sparse data and wavelength intervals)that impact the accuracy of peak-detection were determined. As selecting window sizes reasonably would enhance the accuracy of peak-detection

a derivation method was used to determine the size of the spectral processing window. Then

by setting sampling intervals

spectral window data were re-sampled to obtain sparse spectrum data and perform a polynomial fitting for the sparse data. Finally

the wavelength interval was chosen to determine the coordinates of three points and to search the peak of FBG wavelength. The results of experiment and simulation indicate that the number of spectrum data is three

when the size of processing window is chosen to be 0.4 nm and the re-sampling interval is 0.18 nm. Moreover

when the wavelength interval is chosen to be 0.22 nm

the peak error of proposed method is 0.4 pm

better than that of other methods. It will meet the requirements of FBG sensing systems for demodulating precisely.

关键词

Keywords

references

WADA A, TANAKA S, TAKAHASHI N. Optical fiber vibration sensor using FBG Fabry-Perot interferometer with wavelength scanning and Fourier analysis[J]. IEEE Sensors Journal, 2012, 12(1): 225-229.[2]WU F, ZHAN J, LIU B, et al.. Study on local temperature characteristics of fiber Bragg gratings[J]. Optoelectronics Letters, 2010, 6(2): 98-102.[3]徐国权, 熊代余. 光纤光栅传感技术在工程中的应用\[J\]. 中国光学, 2013,6(3): 306-317.XU G Q, XIONG D Y. Applications of fiber Bragg grating sensing technology in engineering\[J\]. Chinese Optics, 2013,6(3): 306-317. (in Chinese)[4]尚秋峰, 林炳花. 光纤Bragg光栅传感系统典型寻峰算法的比较分析[J]. 电测与仪表, 2010, 47(2): 1-4.SHANG Q F, LIN B H. The comparison and analysis of typical peak-detecting algorithms in fiber bragg grating sensor system[J]. Electrical Measurement and Instrumentation, 2010, 47(2): 1-4.(in Chinese)[5]余有龙, 王学薇, 王浩. 不同采样方式下光纤布拉格光栅反射谱寻峰算法分析[J]. 光子学报, 2012, 41(11): 1274-1278.YU Y L, WANG X W, WANG H. Anaysis of peak-detecting algorithms in fiber bragg grating by different sampling methods[J]. Acta Photonica Sinica, 2012， 41(11)： 1274-1278.(in Chinese)[6]FREDERIC C J, CICERO F M. Peak detection algorithm for fiber bragg grating sensors[C]. Latin America Optics and Photonics Conference, Sao Sebastiao, Brazil: LAOP, 2012: LM2A.8.[7]李敏, 盛毅. 高斯拟合算法在光谱建模中的应用研究[J]. 光谱学与光谱分析, 2008, 28(10): 2352-2355.LI M, SHENG Y. Study on application of Gaussian fitting algorithm to building model of spectral analysis[J]. Spectroscopy and Spectral Analysis, 2008, 28(10): 2352-2355.(in Chinese)[8]韩屏, 周祖德. 高速高精光栅解调器的反馈式相对寻峰算法[J]. 武汉理工大学学报, 2011, 33(1): 10-12.HAN P, ZHOU Z D. Feedback relative peak-detection algorithm for high-speed and high-precision grating demodulator[J]. Journal of Wuhan University of Technology, 2011, 33(1): 10-12.(in Chinese)[9]LUCAS N, ADEMIR N, HYPOLITO K, et al.. Benchmark for peak detection algorithms in fiber bragg grating interrogation and a new neural network for its performance improvement [J]. Sensors, 2011,11(2): 3466-3482.[10]LIU Q, WANG H. Research on the peak-detection algorithm in the high-frequency demodulation for the fiber gating [J]. Journal of Wuhan University of Technology, 2010, 32(2): 59-61.[11]LI Y Q, XIE Y, YAO G Z. Comparison of peak searching algorithms for wavelength demodulation in fiber bragg sensors[C]. Information Engineering and Computer Science, Shanghai, P.R. China: ICIECS, 2010: 1-4.[12]LI M, YAO J P. Multichannel arbitrary-order photonic temporal differentiator for wavelength-decision-multiplexed signal processing using a single FBG [J]. Journal of Lightwave Technology, 2011, 29(17): 2506-2511.[13]王民, 曹绘, 要趁红. 一种改进的小波变换基音检测算法[J]. 重庆邮电大学学报(自然科学版), 2012, 24(3): 283-302.WANG M, CAO H, YAO CH H. Improved arithmetic of wavelet transformation for pitch frequency detection[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2012,24(3):283-302.(in Chinese)[14]SASTRY S S, MALLIKA K, RAO B G, et al.. Liquid crystal textural analysis based on histogram homogeneity and peak detection algorithm[J]. Liquid Crystals, 2012, 39(4): 415-418.[15]SADHUKHAN D, MITRA M. R-peak detection algorithm fore cg using double difference and RR interval processing[J]. Procedia Technology, 2012, 12(4): 873-877.[16]王海涌, 费峥红, 王新龙. 基于高斯分布的星像点精确模拟及质心算法[J]. 光学精密工程, 2009, 17(7):1672-1677.WANG H Y, FEI ZH H, WANG X L. Precise simulation of star spots and centroid calculation based on Gaussian distribution [J]. Opt. Precision Eng., 2009, 17(7):1672-1677.(in Chinese)

浏览量

219

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

高速实时近红外弱信号检测系统

植入光纤布拉格光栅的不同杨氏模量软体材料弯曲测量响应特性

一步超声法金属化封装FBG的传感特性

温度自动补偿超磁致伸缩材料布拉格光栅光纤电流传感器

用于船舶结构监测的大量程光纤布拉格光栅应变传感器