Document Type
Dissertation
Date of Award
Fall 1-31-2014
Degree Name
Doctor of Philosophy in Mathematical Sciences - (Ph.D.)
Department
Mathematical Sciences
First Advisor
Eliza Zoi-Heleni Michalopoulou
Second Advisor
Ali Abdi
Third Advisor
Sunil Kumar Dhar
Fourth Advisor
David James Horntrop
Fifth Advisor
Jonathan H.C. Luke
Abstract
Acoustic signals propagating in the ocean carry information about geometry and environmental parameters within the propagation medium. Accurately retrieving this information leads us to effectively estimate parameters that are of utmost importance in environmental studies, climate monitoring, and defense. This dissertation focuses on the development of sequential Bayesian filtering methods to obtain accurate estimates of instantaneous frequencies using Short Term Fourier Transforms within the acoustic field measured at an array of hydrophones, which can be used in a subsequent step for the estimation of propagation related parameters. We develop a particle filter to estimate these frequencies along with modal amplitudes, variance, model order. In the first part of our work, we consider a Gaussian model for the error in the data measurements, which has been the standard approach in instantaneous frequency estimation to date. We here design a filter that identifies the true structure of the data errors and implement a χ2 model to capture this structure appropriately. We demonstrate both with synthetic and real data that our approach is superior to the conventional method, especially for low Signal-to-Noise-Ratios.
Recommended Citation
Aunsri, Nattapol, "Particle filtering for frequency estimation from acoustic time-series in dispersive media" (2014). Dissertations. 139.
https://digitalcommons.njit.edu/dissertations/139
