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Progress In Eigenvector Beamforming

This paper discusses research in spectrum analysis, beamforming, and direction finding, using signal subspace techniques. Such methods can provide improvements in performance by effectively incorporating prior information concerning the spatial and temporal structure of the signals and noise. During the past decade, many researchers have contributed to broadening the range of applicability of such methods and increasing our understanding of their relation to traditional linear statistical modelling. Representative results will be described, together with an outline of future research directions and a brief introduction to the literature on systolic architectures and algorithms for parallel implementation of the required matrix computations.