Model-based environmental inversion: A shallow water ocean application

Abstract

A model-based approach to invert or estimate the sound-speed profile (SSP) from noisy pressure-field measurements or more succinctly a solution to the model-based inversion problem is discussed. Recall that model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models and noise (shipping, measurement) models into sophisticated processing algorithms. Here the design of a model-based processor (MBP) is investigated based on a Taylor series expansion of the SSP about the most current set of parameter estimates augmented into the state-space representation of a normal-mode propagation model to solve the environmental inversion problem. The resulting processor is adaptive in terms of simultaneously estimating the sound speed, modes, and pressure field at each iteration. Using data obtained from from the well-known Hudson Canyon experiment, a noisy shallow water ocean environment, the processor is designed and the results compared to those predicted using various propagation models and data. It is shown that the on-line, MBP not only predicts the sound speed quite well, but also is able to simultaneously provide enhanced estimates of both modal and pressure-field measurements which are useful for localization and rapid ocean environmental characterization.