Robust Beamforming for Matched Field Processing Under Realistic Environmental Conditions

Abstract

Under ideal known environmental conditions, matched field processing has been shown in a series of papers to be a promising signal processing technique for localizing acoustic sources (see [1] and the references therein). The standard adaptive approaches which have been considered require accurate replica fields finely gridded over the search parameter space for both localization and sidelobe control. Moreover, it is unlikely that detailed knowledge of the ocean environment would be available since it is expensive to obtain highly accurate knowledge of the three dimensional ocean environment (including bottom structure). Finally, it is impractical to generate replica fields incorporating the increasing complexity of a range dependent environment which still remains imperfectly known because of inadequate sampling. To counter these latter issues, we have introduced multiple constraints to the maximum likelihood method (MLM) to construct a beamformer (MCM) more tolerant to environmental mismatch of a deterministic nature. The result is a plane wave or matched field beamformer which accommodates some mismatch in the environment with only a small reduction in resolution while still suppressing sidelobes. In this paper, we briefly present the underlying principles used to derive this beamforming method and follow this with an application to a matched field processing example for a shallow water ocean environment.