Abstract
Adaptive beamformers balance competing demands of enhancing desired signals, suppressing discrete interferers and attenuating background noise. This requires an accurate estimate of the spatial covariance matrix that is challenging to obtain in nonstationary environments. The dominant mode rejection (DMR) beamformer [Abraham & Owsley, Oceans, 1990] addresses this challenge by replacing the sample covariance matrix (SCM) eigenvalues of the noise subspace with the average of these eigenvalues to improve its conditioning. The DMR beamformer requires an estimate of the interferer subspace dimension, and the DMR beamformer's performance degrades when this estimated dimension is inaccurate. We propose the Blended DMR beamformer, whose array weights are formed as a mixture of fixed-dimension DMR beamformer array weights across a range of interferer dimensions. The contribution of each fixed-dimension DMR beamformer is a function of its recent performance in suppressing interferers and attenuating noise. The performance of this blended DMR beamformer approaches that of the best fixed-dimension DMR beamformer in stationary environments, and can outperform any fixed DMR beamformer in many nonstationary environments. [Work supported by ONR Code 321US.]