MODEL-BASED PROCESSOR DESIGN FOR A SHALLOW-WATER OCEAN ACOUSTIC EXPERIMENT

Model-based signal processing is a well-defined methodology enabling t he inclusion of environmental (propagation) models, measurement (senso r arrays) models, and noise (shipping, measurement) models into a soph isticated processing algorithm. Depending on the class of model develo ped from the mathematical representation of the physical phenomenology , various processors can evolve. Here the design of a space-varying, n onstationary, 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 th e 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 i t ideal for this investigation. In this shallow water application, a s tate-space representation of the normal-mode propagation model is used . The processor is designed such that it allows in situ recursive esti mation of both the pressure-field and modal functions. It is shown tha t the MBP can be effectively utilized to ''validate'' the performance of the model on noisy ocean acoustic data. In fact, a set of processor s is designed, one for each source range and the results are quite goo d-implying that the propagation model with measured parameters adequat ely represents the data.