Subspace selection for partially adaptive sensor array processing
J. Scott Goldstein, I.S. Reed · IEEE Transactions on Aerospace and Electronic Systems · 1997
This paper introduces a cross-spectral metric for subspace selection and rank reduction in partially adaptive minimum variance array processing. The counter-intuitive result that it is suboptimal to perform rank reduction via the selection of the subspace formed by the principal eigenvectors of the array covariance matrix is demonstrated. A cross-spectral metric is shown to be the optimal criterion for reduced-rank Wiener filtering.