Use of polynomial chaos expansions in the design of uncertainty-robust matched field processing algorithms

Abstract

The polynomial chaos (PC) expansion method has been recently applied to estimating the statistical properties of underwater acoustic propagation in the presence of environmental uncertainty. Here we use PC estimates of the field covariance structure to design uncertainty robust match field processing (MFP) weights using Krolik’s minimum variance beamformer with sound-speed perturbation constraints (MV-SPC) [J. Acoust. Soc. Am. 92 (3), 1408–1419 (1992)]. The idea behind the MV-SPC beamformer is to realize much of the high sidelobe rejection of MV processors in environments with environmental variability by opening up the signal model to include uncertainty effects. Here we compare the performance of MV-SPC designed with an adiabatic signal uncertainty model to the same beamformer designed with the PC signal model for realizations of the signal vector obtained with fully coupled propagation models, with results showing the superiority of the PC approach. [Work supported by ONR.]