Reconstruction of Fabry-Perot Interferometric Sensor Spectrum From Extremely Sparse Sampling Points Using Dense Neural Network

Bulky fiber spectrum analyzers constrain the application of white light interference-based demodulation of Fabry-Perot interferometric (FPI) sensors in practical measurement scenarios. The key to the miniaturization of the fiber Spectrometer is the reconstruction of the fiber spectrum. However, advances in this field are often characterized by a reliance on microelectromechanical systems (MEMS) that cannot have both excellent frequency and resolution. This letter presented a neural network-based systematic scheme to achieve fiber optic sensor's spectral reconstruction with high-frequency and high-resolution potential. The solution can reconstruct the FPI sensor spectrum consisting of hundreds of points based on extremely sparse (even single-digit) sampling points, with MHz-level frequency, and supports custom tuning the reconstructed wavelength range. This solution can provide a novel platform for designing compact, high-performance fiber spectrum analyzers.