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. Here we investigate the design of a space-varying, nonstationary, model-based processor (MBP) for the Hudson Canyon experiment. 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 the 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 reasonable, implying that the propagation model with measured parameters adequately represents the data.
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