Robust matched-field beamforming with benchmark shallow-water acoustic array data

In this paper, four robust matched-field beamforming methods are evaluated using benchmark shallow-water Mediterranean data. While the Bartlett beamformer, reduced minimum variance beamformer (RMV), and minimum variance beamformer with neighborhood location constraints (MV-NLC) assume a known ocean, the minimum variance beamformer with environmental perturbation constraints (MV-EPC) exploits a model for uncertainties in the ocean parameters. For modest environmental uncertainty, the MV-EPC beamformer is shown to achieve higher probability of correct localization with simulated data and better tracking and sidelobe performance with experimental data than the Bartlett, RMV, and MV-NLC methods