Underwater sound speed inversion by joint artificial neural network and ray theory

Sound speed profiles (SSPs) have a great impact on the accuracy of underwater localization and sonar ranging. In traditional SSP inversion, the sound intensity distribution used in normal mode theory-based matching field processing (MFP) or the multipath signal propagation time adopted in ray theory-based MFP is susceptible to boundary parameter mismatch issues, which reduces the inversion accuracy. Moreover, heuristic algorithms introduced in the MFP require many individuals and iterations to search for the optimal feature representation coefficients after the empirical orthogonal function (EOF) decomposition, which causes extra computational time. In this paper, we propose a two-way interactive signal propagation time measurement method based on an autonomous underwater vehicle (AUV) and a horizontal linear array (HLA), and we apply the propagation time of direct arrival signals for shallow-water SSP inversion to avoid the boundary parameter mismatch. We propose a joint artificial neural network (ANN) and ray theory SSP inversion model to reduce the computational time at the working phase by fitting the nonlinear relationship from the signal propagation time to the SSP, and once the relationship is established, the goal of reducing the computational time can be achieved. To make the ANN better learn the SSP distribution in a target region and ensure a good inversion accuracy, we give an empirical data selection strategy. Then we propose a virtual SSP generation algorithm to help ANN training in the case of under-fitting problems caused by insufficient training data. Simulation results show that our approach can provide a reliable and instantaneous monitoring of shallow-water SSP distribution.