research-article

Performance analysis of the total least squares ESPRIT algorithm

Authors:

B. Ottersten,

M. Viberg,

T. KailathAuthors Info & Claims

IEEE Transactions on Signal Processing, Volume 39, Issue 5

Pages 1122 - 1135

https://doi.org/10.1109/78.80967

Published: 01 May 1991 Publication History

Abstract

The asymptotic distribution of the estimation error for the total least squares (TLS) version of ESPRIT is derived. The application to a uniform linear array is treated in some detail, and a generalization of ESPRIT to include row weighting is discussed. The Cramer-Rao bound (CRB) for the ESPRIT problem formulation is derived and found to coincide with the asymptotic variance of the TLS ESPRIT estimates through numerical examples. A comparison of this method to least squares ESPRIT, MUSIC, and Root-MUSIC as well as to the CRB for a calibrated array is also presented. TLS ESPRIT is found to be competitive with the other methods, and the performance is close to the calibrated CRB for many cases of practical interest. For highly correlated signals, however, the performance deviates significantly from the calibrated CRB. Simulations are included to illustrate the applicability of the theoretical results to a finite number of data

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cover image IEEE Transactions on Signal Processing

IEEE Transactions on Signal Processing Volume 39, Issue 5

May 1991

217 pages

ISSN:1053-587X

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IEEE Press

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Published: 01 May 1991

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