BO-GRNN: Machine Learning for Underwater Acoustic Source Localization

This article proposes a Bayesian optimization-based generalized regression neural network (BO-GRNN) framework for underwater source localization in shallow water environment. Specially, by leveraging kernel regression analysis, BO-GRNN fits a conditional probability density function using training and test samples to infer the source location of unseen test samples. Furthermore, BO-GRNN models the objective function as a Gaussian process (GP) based on observed samples, utilizing an acquisition function to sample around the best objective function values and high variance regions. By evaluating these samples and updating the GP iteratively, the optimal hyperparameters are identified, ultimately improving the training efficiency and localization performance of the GRNN. Simulation experiments and error analysis validate this framework's effectiveness. Further validation using SWellEx-96 experimental data demonstrates the superior localization performance of this framework, even with limited data snapshots. This framework estimates the range and depth of underwater sources within a 10 km range, achieving mean absolute errors of less than 0.15 km for range and 0.2 m for depth. Compared to GRNN, this framework demonstrates a 5 times improvement in training efficiency. These advantages make BO-GRNN an efficient, accurate, and robust solution for underwater source localization. [GRAPHICS]