A model subspace method for matched field source localization in a shallow-water acoustic waveguide

Abstract

Model subspace methods for parameter estimation can yield promising results when temporal or spatial variability dominate the model inputs. Application of Gaussian signal detection theory, summarized by Van Trees, is applied to the localization problem. Model inputs, such as the sound velocity profile in water, bottom properties, and array position, rather than being deterministic, are characterized using a Gaussian random vector. This, in turn, yields a random signal model which is represented as a vector space. Detection and localization via a maximum-likelihood estimator becomes a question of whether the received signal fits the subspace defined by the random signal model.