Deep-Learning-Based Source Localization Using A Local 3D Acoustic Intensity Field in A Sound-Speed Mismatched Deep-Ocean Environment

This article proposes a deep-learning (DL) based method for accurate acoustic source localization in the deep ocean, which leverages a local three-dimensional (3D) acoustic intensity field (AIF) with dimensions of 1.26km×1.26km×504m to estimate the 3D position of a source within a range of 20km. The local AIF can be constructed from measurements from an underwater acoustic sensor network (UASN) using techniques such as optimal interpolation. To achieve accurate 3D localization, a 3D residual convolutional neural network (ResCNN) is designed for processing the AIF. Moreover, the method incorporates a 3D water column sound speed field (SSF) that covers the entire sound propagation path to enhance localization accuracy. To incorporate the environmental information for source localization, two approaches are introduced. One is to implicitly utilize the environmental information through network training based on training data generated in various environments. The second approach explicitly includes the 3D water column SSF to the network input. Since the environmental mismatching problem is inevitable, we discuss the impact of deviations between the adopted SSF and the real SSF on source localization accuracy. The training, validation, and test datasets are generated by the BELLHOP 3D acoustic simulation model. Once the proposed DL-based localization networks are fully trained, their performance outperforms that of a conventional matched-field processing (MFP) localization algorithm for the case where the mismatching factor of SSF obtained through common ocean model estimation ranges from 18% to 29%.