AUV Localization in an Underwater Acoustic Positioning System

This paper develops a Bayesian inversion algorithm for autonomous underwater vehicle (AUV) localization, and carries out a modeling study of several factors contributing to localization accuracy in an underwater acoustic positioning system. The ray-based algorithm estimates AUV position through linearized inversion of transmission arrival-time differences, and provides linearized uncertainty estimates for model parameters. Factors contributing to source localization uncertainty considered here include: (1) modeling transmission paths accounting for refraction due to a depth-varying sounds-peed profile (SSP) instead of using a constant sound-speed approximation and straight-line propagation, (2) inverting for a potential bias in the measured SSP, (3) accounting for errors in hydrophone positions by including these as unknown parameters with prior estimates and uncertainties in the inversion, and (4) applying path-dependent timing correction factors to account for lateral variability in SSP. In each case, non-linear Monte Carlo analysis is applied in which a large number of noisy data sets are inverted to obtain statistical measures of the corresponding localization uncertainties and the improvement that results from addressing these factors. The results from these non-linear analyses are compared to linearized uncertainty estimates from the posterior model covariance matrix. Linearization errors are shown to be negligible in all cases and hence linearized analysis is used to map AUV localization uncertainty as a function of position over the test range.