Frequency analysis of passive sonar signals using multiple-measurement physics-informed neural network

Abstract

We propose a deep learning based frequency analysis for passive sonar signals by considering a linear system based on a relation between time and frequency domain signal representations. An adaptive learned iterative shrinkage thresholding algorithm (Ada-LISTA) is utilized as the deep learning architecture to solve the linear system effectively and to ensure generalization performance. To obtain more reliable solutions, we involve the linear system in loss function during training. Futhermore, in order to reduce noise effectively, we employ multiple measurements from time and space domains, which have common frequency components of passive sonar signals. Thus, the loss function is modified for solutions from the multiple measurements to have shared frequency components. We conduct experiments using synthetic and underwater in-situ data to examine performance of frequency analysis considering the linear system in designing the architecture and finding the optimal weights. The frequency analysis demonstrates superior performance in detecting frequency components and reducing noise, compared to fast Fourier transform and sparse Bayesian learning with low computational burden during its application to the test measurements.