A new approach to weighted subspace fitting using subspace perturbation expansions

Document Type

Conference Proceeding

Date of Original Version



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.

Publication Title, e.g., Journal

Proceedings of SPIE - The International Society for Optical Engineering