The performance of matched-field beamformers with Mediterranean vertical array data

Abstract

Minimum variance (MV) adaptive beamforming has been widely proposed for matched-field processing because it provides a means of suppressing ambiguous beampattern sidelobes. A difficulty with MV methods, however, is their sensitivity to signal wavefront mismatch. In this work, the performance of three robust MV methods and the Bartlett beamformer is evaluated using vertical array data from the Mediterranean Sea collected by the NATO SACLANT Centre. The three MV methods considered are: (1) the reduced MV beamformer (RMV), (2) the MV beamformer with neighborhood location constraints (MV-NLC), (3) the MV beamformer with environmental perturbation constraints (MV-EPC). While the Bartlett, RMV, and MV-NLC methods assume the ocean environment is known precisely, the MV-EPC method models the environment as being random with known statistics. Experimental and companion simulation results indicate that for modest environmental uncertainty, the MV-EPC beamformer achieves a higher probability of correct localization and better sidelobe performance than the other three methods.