This thesis develops a Bayesian inversion algorithm for autonomous
underwater vehicle localization, and carries out a study of several factors
contributing to localization accuracy in an underwater acoustic positioning
system. Specifically, a ray-based algorithm is described that estimates target
position through the linearized inversion of transmission arrival time
differences, and provides linearized uncertainty estimates for model
parameters. Factors contributing to source localization uncertainty considered
here included: (1) modelling transmission paths accounting for refraction due
to a depth-varying SSP instead of using a constant sound-speed approximation
and straight-line propagation, (2) inverting for a potential bias in the measured
sound-speed profile, (3) accounting for errors in hydrophone position by
including these positions as unknown parameters in the inversion, and (4)
applying path-dependent timing correction factors to account for lateral
variability in the sound-speed profile. In each case, nonlinear Monte Carlo
analysis is applied in which a large number of noisy data sets are considered, to
obtain statistical measures of the localization improvement that results by
addressing these factors. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4136 |
| Date | 20 August 2012 |
| Creators | Thomson, Dugald |
| Contributors | Dosso, Stanley Edward |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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