Stochastic description and evaluation of ocean acoustics time-series for frequency and dispersion estimation using particle filtering approach

Journal article

Authors/Editors

KOSIN CHAMNONGTHAI

Strategic Research Themes

Innovative Materials, Manufacturing and Construction (Strategic Research Themes)

Publication Details

Author list: Aunsri N., Chamnongthai K.

Publisher: Elsevier

Publication year: 2021

Journal: Applied Acoustics (0003-682X)

Volume number: 178

ISSN: 0003-682X

eISSN: 1872-910X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102291114&doi=10.1016%2fj.apacoust.2021.108010&partnerID=40&md5=a2a7273cfd1038cf92eacec25791239a

Languages: English-Great Britain (EN-GB)

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Abstract

This paper presents a stochastic description and evaluation of the ocean acoustics signal for the interpretation of the signal in the frequency domain in order to estimate the modal frequency and dispersion curves of the ocean acoustics signal. Thus, this investigation considered the analysis and simulation of an ocean acoustics time-series in the frequency-domain where the signal in the time-domain was corrupted by white Gaussian noise. With a stochastic property of the measured acoustics signal, we derived the accurate posterior probability distribution of the ocean acoustics modal frequencies and then incorporated a mathematical model of the signal propagating in a dispersive media to obtain a likelihood function for a sequential Bayesian filtering framework that is used for modal frequency and dispersion estimation. Sequential Bayesian filtering, a particle filter in particular, is implemented for frequency and dispersion estimation to test the accuracy of the derived model. Demonstrated via simulation results, the proposed method performs excellent estimation as compared to the conventional MAP estimator especially at high noise level. Finally, evaluated by the Monte Carlo root mean squared errors (MCRMSE), the proposed stochastic model offers excellent tracking results with smaller errors than those provided by the existing methods even in low signal-to-noise ratios (SNRs), illustrating the noise robustness and superiority of the proposed model over the other approaches. © 2021 Elsevier Ltd

Keywords

Chi-squared distribution, Frequency estimation, Likelihood function