Various digital-signal-processing algorithms are used to determine the refractive index based on the spectra of Tilted Fibre Bragg Gratings (TFBGs). Identifying new features of the optical spectrum improves estimations of the refractive index. New or modified demodulation algorithms influence measurement accuracy and resolution. In this study, we used signal-processing methods to determine the local and global features of TFBG spectra containing the so-called cladding mode comb. Based on these features, a demodulation method using artificial neural networks was created. The main novelty of this study is the simultaneous use of both local and global spectral features for refractive-index estimation. Currently, these two types of features are used separately. Here, the neural network is used for feature fusion obtained in the first step, consisting of signal-processing methods.
Cięszczyk et al. (Mon,) studied this question.