Fiber Bragg Grating Interrogation System Combining Wavelength-Swept Laser and Convolutional Neural Network

We propose a novel deep learning approach to improve the multipoint performance of fiber Bragg grating (FBG) interrogation systems with high-speed wavelength-swept lasers. Because conventional interrogation systems use wavelength-division multiplexing for multipointing FBGs, the number of FBGs is limited by the wavelength bandwidth of the light source. To overcome this, we attempted to improve the multipoint performance of wavelength-division multiplexing by using multiple FBGs of the same wavelength. However, it is difficult to analyze the peak wavelengths of individual FBGs using a high-speed wavelength-swept laser because the optical spectra of FBGs at the same wavelength are observed to occur in a complex overlapping manner on the microsecond order. Here, we present a peak-detection method based on a convolutional neural network (CNN) in deep learning as a breakthrough in solving this problem. The experiment combines a high-speed wavelength-swept laser and a CNN to achieve high-speed measurement of three overlapping FBGs at the same wavelength. The results demonstrate that a measurement rate of 50 kHz and a wavelength resolution of 4.2 pm can be achieved, improving the limit of wavelength-division multiplexing by a factor of three. Our approach is to simply incorporate peak detection with CNN into existing wavelength-swept laser-based interrogators, which can function as if they were interrogators with several times the multipoint performance.