Data Dimension Reduction Using Krylov Subspaces: Making Adaptive Beamformers Robust to Model Order-Determination

Abstract

In this work, we present a class of low-complexity reduced-dimension adaptive beamformers constructed from expanding Krylov subspaces. We demonstrate how the data dimensionality reduction obtained from Krylov pre-processing decreases the sensitivity of reduced-rank adaptive beamforming techniques to incorrect model-order selection and lessens the computational complexity of systems involving large arrays with many elements. An important advantage of the proposed dimensionality reduction scheme is that it relieves reduced-rank methods from the stringent requirement on the precise model order determination.