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.