Paper
2 October 1998 New approach to weighted subspace fitting using subspace perturbation expansions
Richard J. Vaccaro
Author Affiliations +
Abstract
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard J. Vaccaro "New approach to weighted subspace fitting using subspace perturbation expansions", Proc. SPIE 3461, Advanced Signal Processing Algorithms, Architectures, and Implementations VIII, (2 October 1998); https://doi.org/10.1117/12.325682
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KEYWORDS
Signal to noise ratio

Data modeling

Algorithm development

Array processing

Statistical analysis

Chromium

Error analysis

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