Adaptive signal-subspace algorithms for frequency estimation and tracking

This paper presents an adaptive estimator, and its practical implementations, of the complete noise- or signal-subspace of a sample covariance matrix. 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.