Source localization in the deep ocean using a convolutional neural network

Abstract

In deep-sea source localization, some of the existing methods only estimate the source range, while the others produce large errors in distance estimation when estimating both the range and depth. Here, a convolutional neural network-based method with high accuracy is introduced, in which the source localization problem is solved as a regression problem. The proposed neural network is trained by a normalized acoustic matrix and used to predict the source position. Experimental data from the western Pacific indicate that this method performs satisfactorily: the mean absolute percentage error of the range is 2.10%, while that of the depth is 3.08%.