Abstract
A stochastic generalization of matched-field processing (MFP) is presented that incorporates stochastic steering matrices rather than steering vectors. This provides a natural framework for including environmental uncertainty associated with incompletely known environmental knowledge. Parametric probabilities allow use of polynomial chaos (PC) expansions to describe environmental uncertainty in a rigorous manner, yielding efficient representations of the stochastic steering matrices through the application of sparse sampling methods. In particular, PC methods apply to the uncertainty of sediment variability that can be modeled via horizontal spectral methods. With moderate variability over scales justified by data in the New Jersey shelf region, the sediment uncertainty can be modeled with relatively few PC expansion coefficients, which depend only on the mean amplitude of spectral components. The coefficients express the nonlinear dependence of the acoustic field on the sediment variability, essentially orthonormalizing higher moments. The coefficients lead immediately to stochastic steering matrices, which are then compared via various MFP processors against acoustic data for MFP source localization. [Work supported by the U.S. Office of Naval Research.]
Published Version
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