Identification of linear systems from noisy data

Linear dynamic errors-in-variables models with mutually uncorrelated noise components are considered. A main complication in identification is that the systems are not uniquely determined from the (ensemble) second moments of the observations. The authors analyze certain properties of the set of all observationally equivalent systems. In addition, they describe the sets of spectral densities corresponding to a given Frisch corank. The results obtained are of importance for developing and analyzing identification algorithms