Model-based processor design for a shallow water ocean acoustic experiment

Abstract

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 a sophisticated processing algorithm. Depending on the class of model developed from the mathematical representation of the physical phenomenology, various processors can evolve. Here the design of a space-varying, nonstationary, model-based processor (MBP) is investigated and applied to the data from a well-controlled shallow water experiment performed at Hudson Canyon. This particular experiment is very attractive for the inaugural application of the MBP because it was performed in shallow water at low frequency requiring a small number of modes. In essence, the Hudson Canyon represents a well-known ocean environment, making it ideal for this investigation. In this shallow water application, a state-space representation of the normal-mode propagation model is used. The processor is designed such that it allows in situ recursive estimation of both the pressure-field and modal functions. It is shown that the MBP can be effectively utilized to ‘‘validate’’ the performance of the model on noisy ocean acoustic data. In fact, a set of processors is designed, one for each source range and the results are quite good—implying that the propagation model with measured parameters adequately represents the data.