Robust matched field processing for array tilt and environmental mismatch

Abstract

Adaptive matched field processing with the minimum variance beamformer provides excellent sidelobe suppression for source localization, but suffers from sensitivity to mismatch between the modeled and true acoustic field (i.e., environmental mismatch). To increase tolerance to the mismatch while retaining satisfactory sidelobe control, robust algorithms such as the white noise constraint (WNC) can be employed. The WNC alone, however, is not sufficient when the mismatch results from an unknown array tilt (i.e., geometric mismatch). This study introduces an adaptive matched field beamformer that is tolerant to both array tilt and environmental mismatch. By modeling the pressure fields corresponding to a set of assumed tilt angles, we impose multiple constraints that, when applied to the beamformer, increase robustness to the array tilt. Simulations and data results are presented to demonstrate localization and tracking of a surface ship (200–500 Hz) using a 16-element, 56-m long, tilted vertical array in approximately 100-m deep shallow water.