Mean squared error performance of adaptive matched field localization under environmental uncertainty

Matched field processing (MFP) is the use of full-field acoustic modeling to obtain improved detection and localization over conventional planewave and range focused beamforming in passive sonar signal processing. MFP localization (MFL), however, is a challenge in practice due to high ambiguities in the search surface that introduce large errors. In addition, uncertainties in environmental characterizations lead to mismatched field replicas that ultimately limit localization performance. Also, the adaptive nature of MFP requires use of estimated data covariances whose impact must be accounted for. The goal of this paper is to use the method of interval errors (MIE) to predict mean-squared error localization performance of MFL at moderate to low SNRs in the presence of mismatch, to assess system performance and sensitivities.