Underwater Source Localization via Spectral Element Acoustic Field Estimation

Underwater source localization (USL) from a passive array of acoustic sensors is a challenging problem, especially in complex environments characterized by multipath and reverberation effects, irregular seabed geometry, and low signal-to-noise ratio (SNR). This article proposes a recursive Bayesian approach that propagates a spectral-element approximation of the wave equation to model the discretized space–time dynamics of the acoustic field conditioned on the position of the source, and sequentially estimates the field and the position of the radiating source from the acoustic measurements. We pursue a multiple-model approach where each model assumes either the source absence or its presence within a specific spectral element. To handle the high dimension of the large-scale field estimation problem and reduce the computational complexity, the multiple-model filter is implemented using ensemble Kalman filters (EnKFs). Finally, the effectiveness of the proposed multiple-model spectral-element ensemble Kalman filter is demonstrated through simulation experiments in underwater acoustic environments with regular and irregular seabed geometry and via comparison with the standard matched-field processing (MFP) method.