Overlapping Spectrum Classification and Demodulation of Fiber Bragg Grating Sensing Network Based on CWT-PSO Algorithm

In this study, we developed a novel fiber Bragg grating (FBG) sensing network system that flexibly configures the number of sensors according to the priority of the monitored area, thus, improving the bandwidth utilization efficiency and increasing the number of sensors in the priority area. Because of the differences in the degree of spectral overlap of each channel, it is essential to achieve fast classification and accurate demodulation of overlapping spectra. The continuous wavelet transform (CWT) -particle swarm optimization (PSO) algorithm was used to achieve the overlapping spectrum classification and demodulation of the FBG sensing network. First, CWT was used to segment the spectral signals, and the overlapping spectra were classified according to their characteristics. Then, PSO was used to demodulate multiple FBG overlapping spectra. The simulation results show that the proposed method effectively decreases the demodulation time, and the maximum demodulation error is within 10 pm. This study provides an approach for fast and accurate demodulation of overlapping spectra in large-capacity FBG sensing networks.