Yule-Walker equations in the Frisch scheme solution of errors-in-variables identification problems

A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and output are affected by additive white noise, is proposed. This approach takes advantage of the properties of both the dynamic Frisch scheme and Yule-Walker equations and allows to identify the system parameters and the noise variances in a congruent way since the estimates assure the positive definiteness of the autocorrelation matrix of the EIV process. The effectiveness of the method has been tested by means of Monte Carlo simulations and compared with those of other EIV identification methods. The proposed procedure is characterized by a good compromise between estimation accuracy and computational efficiency.