Mathematical Problems in Engineering
Volume 2003 (2003), Issue 3, Pages 93-101
A simple algorithm is developed for unbiased parameter
identification of autoregressive (AR) signals subject to white
measurement noise. It is shown that the corrupting noise
variance, which determines the bias in the standard least-squares
(LS) parameter estimator, can be estimated by simply using the
expected LS errors when the ratio between the driving noise
variance and the corrupting noise variance is known or obtainable
in some way. Then an LS-based algorithm is established via the
principle of bias compensation. Compared with the other LS-based
algorithms recently developed, the introduced algorithm requires
fewer computations and has a simpler algorithmic structure.
Moreover, it can produce better AR parameter estimates whenever a
reasonable guess of the noise variance ratio is available.