Adaptive wavenumber estimation for mode tracking in a shallow ocean environment

The shallow ocean is an uncertain, varying, dispersive environment dominated by ambient and shipping noise as well as temperature fluctuations that alter sound propagation throughout creating a large number of environmental variations. The need to develop processors that are capable of tracking these changes while simultaneously providing enhanced signals implies a stochastic as well as an adaptive design is required. The stochastic requirement follows directly from the multitude of variations created by uncertain parameters and noise. An adaptive processor providing enhanced signal estimates for acoustic hydrophone measurements on a vertical array as well as enhanced modal function and wavenumber estimates is developed. A normal-mode model is transformed to state-space form and incorporated directly into the processor enabling the signal enhancement capabilities. Data synthesized from the well-known Hudson Canyon experiment is used to demonstrate the viability of this approach.

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