Sequential Bayesian Filtering in Ocean Acoustics

Sequential filtering provides an optimal framework for estimating and updating the unknown parameters of a system as data become available. Despite significant progress in the general theory and implementation, sequential Bayesian filters have been sparsely applied to ocean acoustics. The foundations of sequential Bayesian filtering with emphasis on practical issues are first presented covering both Kalman and particle filter approaches. Filtering becomes a powerful estimation tool, employing prediction from previous estimates and updates stemming from physical and statistical models that relate acoustic measurements to the unknown parameters. Ocean acoustic applications are then discussed focusing on the estimation of environmental parameters evolving in time or space. The potential of particle filtering in ocean acoustics is further demonstrated through application to experimental data from the Shallow Water 2006 experiment.