Underwater Source Localization via Spectral Element Acoustic Field Estimation

We design a multiple-model state estimator where each filter runs a propagation model with a different source term
associated to the hypothesis of the source being positioned in a specific element of the discretized domain. A null hyphotesis is added to account for the absence of the source in the domain. The decision on the propagation model (i.e., source position) that is more likely given the available acoustic measurements is taken based on the mode probabilities associated to each hypothesis. To handle the high dimension of the large-scale field estimation problem and reduce the computational complexity, the multiple-model filter is implemented by using the ensemble Kalman filter. 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 method.