@TechReport{ it:2016-016,
author = {Torsten S{\"o}derstr{\"o}m and Umberto Soverini},
title = {Errors-in-Variables Identification using Maximum
Likelihood Estimation in the Frequency Domain},
institution = {Department of Information Technology, Uppsala University},
department = {Division of Systems and Control},
year = {2016},
number = {2016-016},
month = sep,
abstract = {This report deals with the identification of
errors-in-variables (EIV) models corrupted by additive and
uncorrelated white Gaussian noises when the noise--free
input is an arbitrary signal, not required to be periodic.
In particular, a frequency domain maximum likelihood (ML)
estimator is proposed and analyzed in some detail. As some
other EIV estimators, this method assumes that the ratio of
the noise variances is known. The estimation problem is
formulated in the frequency domain. It is shown that the
parameter estimates are consistent. An explicit algorithm
for computing the asymptotic covariance matrix of the
parameter estimates is derived. The possibility to
effectively use lowpass filtered data by using only part of
the frequency domain is discussed, analyzed and
illustrated. }
}