Source localization by Matching the Multipath Arrival Angles based on Sparse Bayesian Learning

Abstract

Abstract In the deep-sea direct arrival region, a significant multipath characteristic is presented by the propagation of acoustic signals. This characteristic is closely linked to the distance and depth of the sound source and can be utilized for source localization. In this study, the changes in incidence angle characteristics of multipath signals at different distances and depths are analysed initially. Subsequently, the high-resolution azimuthal spectra are obtained using the Sparse Bayesian Learning (SBL) method. The azimuthal spectra are then matched with multiple incidence angles using the Gaussian kernel function, facilitating the localization of sound sources. Throughout this paper, the impacts of different distance, depth, and SNR conditions on the model are assessed through simulations. Furthermore, the model’s validity is confirmed by utilizing experimental data from an explosion sound source.