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Proceedings Paper

Asymptotic Analysis Of The Total Least Squares ESPRIT Algorithm'
Author(s): B. Ottersten; M. Viberg; T. Kailath
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Paper Abstract

This paper considers the problem of estimating the parameters of multiple narrowband signals arriving at an array of sensors. Modern approaches to this problem often involve costly procedures for calculating the estimates. The ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm was recently proposed as a means for obtaining accurate estimates without requiring a costly search of the parameter space. This method utilizes an array invariance to arrive at a computationally efficient multidimensional estimation procedure. Herein, the asymptotic distribution of the estimation error is derived for the Total Least Squares (TLS) version of ESPRIT. The Cramer-Rao Bound (CRB) for the ESPRIT problem formulation is also derived and found to coincide with the variance of the asymptotic distribution through numerical examples. The method is also compared to least squares ESPRIT and MUSIC as well as to the CRB for a calibrated array. Simulations indicate that the theoretic expressions can be used to accurately predict the performance of the algorithm.

Paper Details

Date Published: 14 November 1989
PDF: 12 pages
Proc. SPIE 1152, Advanced Algorithms and Architectures for Signal Processing IV, (14 November 1989); doi: 10.1117/12.962273
Show Author Affiliations
B. Ottersten, Stanford University (United States)
M. Viberg, Stanford University (United States)
T. Kailath, Stanford University (United States)

Published in SPIE Proceedings Vol. 1152:
Advanced Algorithms and Architectures for Signal Processing IV
Franklin T. Luk, Editor(s)

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