Sound source ranging using a feed-forward neural network trained with fitting-based early stopping

Abstract

When a feed-forward neural network (FNN) is trained for acoustic source ranging in an ocean waveguide, it is difficult evaluating the FNN ranging accuracy of unlabeled test data. The label is the distance between source and receiver array. A fitting-based early stopping (FEAST) method is introduced to evaluate the FNN ranging error on test data where the distance to the source is unknown. Based on FEAST, when the evaluated ranging error is minimum on test data, training is stopped. This will improve the FNN ranging accuracy on the test data. The FEAST is demonstrated on simulated and experimental data.