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An Overview of Sequential Bayesian Filtering



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This article has been accepted for inclusion in a future issue of this journal Content is final as presented with the exception of pagination IEEE JOURNAL OF OCEANIC ENGINEERING 1 Peer Reviewed Technical Communication An Overview of Sequential Bayesian Filtering in Ocean Acoustics Caglar Yardim Member IEEE Zoi Heleni Michalopoulou Senior Member IEEE and Peter Gerstoft 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 Index Terms Acoustic signal processing acoustic tracking ensemble Kalman filter extended Kalman filter EKF ocean acoustics particle filter PF sequential importance resampling SIR sequential Monte Carlo methods unscented Kalman filter UKF I INTRODUCTION A common feature of inverse problems in ocean acoustics is that underlying physical parameters are estimated from measured acoustic data Examples include source localization 1 4 geoacoustic inversion 5 9 and marine mammal signal processing 10 In a Bayesian framework prior knowledge and acoustic models are combined with a likelihood function to provide posterior probability density functions pdfs of parameters of interest This formulation was first proposed in source localization 11 12 Geoacoustic inversion was subsequently approached in a similar fashion estimating in addition to source location



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