Combining the Frisch scheme and Yule-Walker equations for identifying multivariable errors-in-variables models

Errors–in–Variables (EIV) models, i.e. models whose stochastic environment considers measurement errors on both inputs and outputs are intrinsically more realistic than representations assuming an exact knowledge of the input but are also more difficult to estimate. The difficulties increase in a non trivial way passing from the SISO and MISO cases to the MIMO one. This paper proposes a procedure for EIV identification of MIMO processes based on the Frisch scheme that assumes additional white noises on all inputs and outputs and shows its effectiveness by means of Monte Carlo simulations.