Identification of errors-in-variables systems using data clustering

The fact that simultaneous estimation of process and noise parameters using second-order properties is not possible under fairly general conditions is a well-known result in literature in the context of dynamic errors-in-variables systems. In order to make systems identifiable, additional restrictions have to be imposed. One possibility is that data are separable into two distinct clusters, which can be independently identified and the estimated parameters compared. This paper outlines an approach to system identification using principal component analysis to cluster data and the generalized Koopmans-Levin method to derive parameter estimates.