Parametrically adaptive wavenumber processing for mode tracking in a shallow ocean experiment

The shallow ocean is a dynamic environment requiring an adaptive processor. Parametrically adaptive processing implies embedding a parametric process model enabling a joint sequential processor capable of tracking oceanic variations. Here we address the problem of estimating or tracking modal functions in the ocean while jointly adjusting (adaptively) the inherent normal-mode propagation model parameters (wavenumbers) based on the data available from the Hudson Canyon experiment.