Frequency-wavenumber spectrum estimation using blended dominant mode rejection beamforming

Abstract

Capon [Proc. IEEE (1969)] designed the minimum variance distortionless response (MVDR) beamformer to obtain spectral estimates with better resolution than the conventional averaged-periodogram estimator. The MVDR spectrum is a function of the inverse of the sample covariance matrix (SCM), which often must be regularized prior to inversion. To address conditioning problems, Abraham and Owsley [IEEE Oceans (1990)] developed a modified MVDR approach called dominant mode rejection (DMR). The DMR beamformer defines its weights using a structured covariance consisting of a low-rank interference subspace plus an orthogonal noise subspace. It assumes the rank of the interference is known. Recently, Buck and Singer [IEEE SAM (2018)] proposed the blended DMR beamformer that eliminates the need for rank estimation by defining a weight vector that is an affine combination of fixed-rank DMR beamformers. This talk investigates frequency-wavenumber estimation using blended DMR, focusing particularly on efficient implementations for large linear arrays. Adapting the approach described by Therrien [Prentice Hall (1992)] for MVDR, the blended DMR spectrum for an equally spaced array can be computed using fast Fourier transforms of the sample eigenvectors. Results will be illustrated using experimental data from underwater vertical arrays. [Work supported by ONR.]