Robust Broadband Adaptive Beamforming via Polynomial Eigenvalues

Abstract

A novel technique for robust broadband adaptive beamforming (ABF) is proposed. The technique, referred to as domain-weighted polynomial matrix eigenvalue decomposition (DW-PEVD), is founded on a basic paradigm shift from one of broadband noise cancellation to one of signal separation. It uses the second-order sequential best rotation (SBR2) algorithm to perform second order convolutive blind signal separation after applying a simple transformation to the data. The transformation is designed to exploit prior knowledge in the form of an estimated steering vector. The method is quite distinct from existing algorithms for robust broadband ABF and can offer improved performance in many cases. The results of some computer simulations that demonstrate this point are presented