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A Stable Phantom Material for Optical and Acoustic Imaging

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Published on: June 16, 2023

Particle smoothers in sequential geoacoustic inversion.

Caglar Yardim¹, Peter Gerstoft, William S Hodgkiss

¹Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA. cyardim@ucsd.edu

The Journal of the Acoustical Society of America

|August 10, 2013

Summary

Sequential Bayesian methods, like particle filters and smoothers, improve tracking of moving sources in dynamic ocean environments by reducing parameter uncertainty. These advanced techniques enhance accuracy by utilizing both past and future data for more precise localization.

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Area of Science:

Oceanography
Signal Processing
Statistical Inference

Background:

Particle filters are established sequential Bayesian methods for tracking unknown variables in complex environments.
These methods model sources and environmental parameters as non-stationary random variables.
Particle filters handle nonlinear systems and non-Gaussian probability distributions.

Purpose of the Study:

To apply and evaluate particle smoother algorithms for geoacoustic and source tracking.
To compare the performance of forward-backward and two-filter smoothers.
To assess the reduction in parameter uncertainty achieved by smoothing compared to filtering.

Main Methods:

Utilized sequential Bayesian inference, specifically particle filters and particle smoothers.
Implemented forward-backward smoother and two-filter smoother algorithms.
Applied methods to experimental data from SWellEx-96 and SW06 ocean experiments.

Main Results:

Particle smoothers were successfully applied to real-world ocean tracking data.
Both smoother algorithms demonstrated a reduction in parameter uncertainty compared to filtering alone.
The study validated the effectiveness of smoothing in improving source localization accuracy.

Conclusions:

Particle smoothers offer enhanced performance over particle filters for source tracking in dynamic ocean environments.
The use of both past and future data in smoothers significantly reduces parameter uncertainty.
These findings have implications for improved underwater navigation and acoustic monitoring.