An overview of sequential bayesian filtering in ocean acoustics

Document Type

Article

Publication Date

1-1-2011

Abstract

Sequential filtering provides a suitable framework for estimating and updating the unknown parameters of a system as data become available. The foundations of sequential Bayesian filtering with emphasis on practical issues are first reviewed covering both Kalman and particle filter approaches. Filtering is demonstrated to be 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 reviewed focusing on source tracking, estimation of environmental parameters evolving in time or space, and frequency tracking. Spatial arrival time tracking is illustrated with 2006 Shallow Water Experiment data. © 2011 IEEE.

Identifier

79952992262 (Scopus)

Publication Title

IEEE Journal of Oceanic Engineering

External Full Text Location

https://doi.org/10.1109/JOE.2010.2098810

ISSN

03649059

First Page

71

Last Page

89

Issue

1

Volume

36

Grant

N00014-05-1-0262

Fund Ref

Office of Naval Research

This document is currently not available here.

Share

COinS