TY - JOUR
T1 - An overview of sequential bayesian filtering in ocean acoustics
AU - Yardim, Caglar
AU - Michalopoulou, Zoi Heleni
AU - Gerstoft, Peter
N1 - Funding Information:
Manuscript received September 02, 2010; revised November 21, 2010; accepted December 03, 2010. Date of publication February 17, 2011; date of current version March 18, 2011. This work was supported by the U.S. Office of Naval Research Code 32, under Grants N00014-09-1-0313, N00015-05-1-0264, N00014-05-1-0262, and N00014-10-1-0073. Associate Editor: R. Chapman.
PY - 2011/1
Y1 - 2011/1
N2 - 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.
AB - 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.
KW - Acoustic signal processing
KW - acoustic tracking
KW - ensemble Kalman filter
KW - extended Kalman filter (EKF)
KW - ocean acoustics
KW - particle filter (PF)
KW - sequential Monte Carlo methods
KW - sequential importance resampling SIR)
KW - unscented Kalman filter (UKF)
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U2 - 10.1109/JOE.2010.2098810
DO - 10.1109/JOE.2010.2098810
M3 - Review article
AN - SCOPUS:79952992262
SN - 0364-9059
VL - 36
SP - 71
EP - 89
JO - IEEE Journal of Oceanic Engineering
JF - IEEE Journal of Oceanic Engineering
IS - 1
M1 - 5713818
ER -