Acoustic source localization using Bayesian optimization with a Gaussian process surrogate model

Abstract

Matched field processing is a computationally expensive approach to estimate acoustic source location, since it relies on a grid search through range and depth. We present an efficient alternative that samples the parameter space with a Bayesian approach using a Gaussian process as a surrogate model of the objective function. The objective function is defined as a Bartlett processor whose output measures the match between a received and replica pressure field on a vertical line array. Replica fields are obtained using a normal mode propagation model whose geoacoustic parameters are selected from the parameter search space. The surrogate model represents the posterior on the objective function and is updated with each model evaluation. Optimization is performed with sequential model evaluations, with an acquisition function guiding the next point in parameter space to be evaluated. We demonstrate our approach using both simulations and real data collected during the SWELLEX96 ocean acoustics experiment. Results indicate Bayesian optimization using the GP surrogate model converges on average within 700 evaluations of the objective function, far fewer than required in matched field processing.