基于大数据分析的低频电磁信号光纤传感检测装置设计

为了提高低频电磁信号光纤传感检测能力,提出一种基于大数据分析的低频电磁信号光纤传感检测装置,采用光纤传感器进行低频电磁信号采集,构建低频电磁信号的时频特征分析模型,提取光纤传感检测的低频电磁信号谱特征量,采用大数据融合方法进行光纤传感检测的低频电磁信号谱特征融合,构建光纤传感检测的低频电磁信号时频分解模型,采用快速分数阶傅里叶变换实现低频电磁信号光纤传感检测和噪声分离,结合盲源检测结果,提高对低频电磁信号的光纤传感检测能力。仿真结果表明,采用该方法进行低频电磁信号光纤传感检测的准确性较高,联合感知能力较强,信号的主瓣展宽较高,说明检测的抗干扰性较好。

Design of fiber optic sensor detecting device for low frequency electromagnetic signal based on big data analysis

In order to improve the detection capability of the low-frequency electromagnetic signal optical fiber,a lowfrequency electromagnetic signal fiber sensing detection device based on large data analysis is proposed,the low-frequency electromagnetic signal acquisition is carried out by using an optical fiber sensor,a time-frequency characteristic analysis model of a low-frequency electromagnetic signal is constructed,the low-frequency electromagnetic signal spectrum characteristic amount detected by the optical fiber sensing is extracted,the low-frequency electromagnetic signal spectrum characteristic fusion of the optical fiber sensing detection is carried out by adopting a large-data fusion method,and a low-frequency electromagnetic signal time-frequency decomposition model of the optical fiber sensing detection is constructed,The low-frequency electromagnetic signal fiber sensing detection and the noise separation are realized by the fast fractional Fourier transform,and the optical fiber sensing detection capability of the low-frequency electromagnetic signal is improved in combination with the detection result of the blind source.The simulation results show that the method can be used for low-frequency electromagnetic signal fiber sensing detection with high accuracy,strong joint sensing capability,high main lobe extension of the signal,and good anti-interference performance.