An adaptive estimator, and its practical implementations, of the complete noise- or signal-subspace of a sample covariance matrix are presented. The general formulation of the proposed estimator results from an asymptotic argument which shows the signal- or noise-subspace computation to be equivalent to a constrained gradient search procedure. Two categories of unbiased estimators of the gradient, possessing varying degrees of complexity, are presented and the convergence rates of these estimates are discussed.
|Original language||English (US)|
|Number of pages||4|
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|State||Published - Jan 1 1987|