Deterministic performance analysis of subspace methods for cisoid parameter estimation

Authors

Céline Aubel and Helmut Bölcskei

Reference

Proc. of IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, pp. 1551-1555, July 2016.

[BibTeX, LaTeX, and HTML Reference]

Abstract

Performance analyses of subspace algorithms for cisoid parameter estimation available in the literature are predominantly of statistical nature with a focus on asymptotic—either in the sample size or the SNR—statements. This paper presents a deterministic, finite sample size, and finite–SNR performance analysis of the ESPRIT algorithm and the matrix pencil method. Our results are based, inter alia, on a new upper bound on the condition number of Vandermonde matrices with nodes inside the unit disk. This bound is obtained through a generalization of Hilbert’s inequality frequently used in large sieve theory.

Keywords

Subspace methods, ESPRIT, matrix pencil, condition number of Vandermonde matrices


Download this document:

 

Copyright Notice: © 2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.