IDENTIFICATION OF A CLASS OF DYNAMIC ERRORS-IN-VARIABLES MODELS

Dynamic errors-in-variables (EV) models are a new type of linear system models and have found extensive practical applications. One common and important concern with EV models is how to remove noise-induced bias in parameter estimators. In this paper some significant extensions to the newly established bias-eliminated least-squares (BELS) method are made, so that this BELS method can be applied to unbiased identification of a general class of dynamic EV models where input noise is white noise and output noise is correlated noise but the noise statistics are unknown a priori. Though still based on the bias correction principle, this method is very meaningful in that it presents a novel and efficient way of utilizing signal-processing techniques to draw much more useful information from sampled data in order to get desirable identification results. The performance of the proposed method is illustrated by numerical examples.