A summary of filtering approaches in ocean acoustics.

Abstract

Sequential filtering provides an optimal framework for estimating and updating the unknown parameters of a system as data become available. Filtering is a powerful estimation tool, employing prediction from previous es‐timates and updates stemming from physical and statistical models that relate acoustic measurements to the unknown parameters. The foundations of sequential Bayesian filtering with emphasis on practical issues are presented covering both Kalman and particle filter approaches. Extended, unscented, and ensemble Kalman filters are compared to particle filtering approaches such as sequential importance resampling and advanced variants. Complex problems in ocean acoustics, where the state vector order is uncertain or the most suitable physical model for the problem at hand is not known a priori, are also addressed. Examples of model order selection and multiple model particle filters in underwater acoustic applications are given. Ocean acoustic examples are presented in target tracking, wave estimation, geoacoustic inversion, and frequency tracking.