Signal Processing 2020 DOI: 10.1016/j.sigpro.2019.107300 View full text
Olivier Besson, François Vincent

Abstract: Reduced-rank adaptive beamforming is a well established and efficient methodology, notably for disturbance covariance matrices which are the sum of a strong low-rank component (interference) and a scaled identity matrix (thermal noise). Eigenvalue or singular decomposition is often used to achieve rank reduction. In this paper, we study and analyze an alternative, namely a partial Cholesky factorization, as a means to retrieve interference subspace and to compute reduced-rank beamformers. First, we study the a…

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