New approach to weighted subspace fitting using subspace perturbation expansions
Richard J. Vaccaro · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
Weighted Subspace Fitting (WSF) is a method of estimating signal parameters from a subspace of a matrix of received data. WSF was originally derived using the asymptotic statistics of sample eigenvectors. This paper presents a new approach to deriving statistically optimal for WSF algorithms. The approach uses a formula called a 'subspace perturbation expansion', which shows how the subspaces of a finite-size matrix change when the matrix elements are perturbed. The perturbation expansion is used to derive an optimal WSF cost function for estimating directions of arrival in array signal processing. The resulting cost function is identical to that obtained using asymptotic statistics.