Weighted subspace fitting using subspace perturbation expansions

This paper presents a new approach to deriving statistically optimal weights for weighted subspace fitting (WSF) algorithms. The approach uses a formula called a subspace perturbation expansion, which shows how the subspaces of a matrix change when the matrix elements are perturbed. The perturbation expansion is used to derive an optimal WSF algorithm for estimating directions of arrival in array signal processing. © 1998 IEEE

A new approach to weighted subspace fitting using subspace perturbation expansions

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 (large data length) statistics of sample eigenvectors. This paper presents a new approach to deriving statistically optimal weights for weighted subspace fitting (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