Minimum variance distortionless response beamforming of acoustic array data

Abstract

An array of hydrophones is towed below the sea surface so as to sample the underwater acoustic pressure field in both space and time, while a land-based array of microphones is used to sense the atmospheric acoustic environment which, at the time, was dominated by a single source of broadband energy. After transformation from the time domain to the frequency domain, the sensor outputs from each array are weighted and combined in the spatial domain (beamformed) so as to produce a frequency–wave number power spectrum, which displays the power spectral density distribution of the various signal and noise sources as a joint function of frequency and wave number. The frequency-domain beamforming (or spatial filtering) process enables both conventional and optimal estimation of the frequency–wave number power spectrum. The optimal spatial filtering technique used here is commonly referred to as the Minimum Variance Distortionless Response (MVDR) beamformer which requires inversion of the observed narrow-band cross-power spectral matrix at each frequency of interest. A comparison of the frequency–wave number power spectra estimated by the two spatial filtering techniques shows that the MVDR beamformer enables the various sources of acoustic energy to be more clearly delineated in frequency–wave number space. The MVDR beamformer is a data-adaptive spatial filter which is observed to suppress sidelobes, to enhance the spatial resolution of an array through narrower beamwidths, and to provide superdirective array gain at frequencies well below the design frequency of an array. By extending the processing to include the data from another type of towed array, it is shown that frequency–wave number analysis, when incorporated with MVDR beamforming, constitutes a powerful diagnostic tool for studying the self-noise characteristics of towed arrays.