摘要
大容量FBG传感网络解调系统中,信号去噪是影响FBG传感系统精确解调的重要因素。为了提高解调精度,本文提出一种以改进变分模态分解结合可调节半软阈值方法用于处理光纤信号。变分模态分解(VMD)将输入的信号分解为给定数量K个固有模态数(IMF),设置错误的K值会导致性能下降。本文在VMD的基础上添加4条约束条件,使其自适应的选取K值并减少了VMD的计算量,然后用新阈值函数进行去噪处理,最后通过温度测试实验,得到波长与温度的线性相关系数,评估性能与应用价值。实验结果表明,本文函数在连续性以及信号还原度均取得良好的效果。信噪比(SNR)相比于传统软、硬阈值平均提升了6.0162 dB、13.0003 dB,均方根误差(RMSE)平均降低了0.0442、0.1487。温度测试实验中温度与波长的线性相关系数为0.9992,能满足实际工程需求。
In the large-capacity FBG sensor network demodulation system,signal denoising is an important factor that affects the accurate demodulation of the FBG sensor system.In order to improve the accuracy of demodulation,an improved variational modal decomposition combined with an adjustable semi-soft threshold method for processing optical fiber signals is proposed in this paper.Four constraints are added to VMD to make its adaptive selected K values and reduce the calculation amount of VMD.Then denoising is performed with the new threshold function.Finally,through the temperature test experiment,the linear correlation coefficient between the wavelength and the temperature is obtained,and the performance and application value are evaluated.The experimental results show that the method in this paper achieves good results in both continuity and signal reduction.Compared with the traditional soft and hard thresholds,the signal-to-noise ratio(SNR)has increased by 6.0162 dB and 13.003 dB on average,and the root mean square error(RMSE)has been reduced by 0.0442 and 0.1487 on average.The linear correlation coefficient between temperature and wavelength in the temperature test experiment is 0.9992,which has better application value.
作者
邵向鑫
路天麒
马子筱
江虹
马柯异
SHAO Xiang-xin;LU Tian-qi;MA Zi-xiao;JIANG Hong;MA Ke-yi(School of Electrical and Electronic Engineering,Changchun University of Technology,Changchun 130012,China;School of Automation Engineering,Northeast Dianli University,Jilin 132012,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2022年第1期84-88,共5页
Laser & Infrared
基金
吉林省科技发展计划项目(No.20190302085GX)
吉林省教育厅“十三五”科学技术项目(JJKH20200670KJ)资助。
关键词
FBG信号
变分模态分解
小波阈值去噪
新阈值函数
FBG signal
variational modal decomposition
wavelet threshold denoising
new threshold function
