Deterministic performance analysis of subspace methods for cisoid parameter estimation


Céline Aubel and Helmut Bölcskei


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

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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.


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

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