Source Ranging Using Attention-Based Convolutional Neural Network

Source ranging based on ship-radiated noise is a crucial task in many practical applications. Deep neural networks (DNNs) have shown outstanding performance but poor interpretability on source ranging, leading to the heavily hidden risks of blind trust in the AI black box. In this study, an attention-based convolutional neural network (ABCNN) is proposed for the ship ranging in an attempt to visualize the features of concern in neural networks. Acoustic data of four ships were collected during a sea trial conducted in January 2021 to validate the ship ranging performance of ABCNN. Results showed high accuracy in ship ranging using synthetic data and part of the experimental data as a training set for the proposed method. The attention mechanism visualized a concentration on the inherent features of ships and the waveguide effect of underwater acoustic channels.