Identification methods in a unified framework

The paper derives a framework suitable to discuss the classical Koopmans-Levin (KL) and maximum likelihood (ML) algorithms to estimate parameters of errors-in-variables linear models in a unified way. Using the capability of the unified approach a new parameter estimation algorithm is presented offering flexibility to ensure acceptable variance in the estimated parameters. The developed algorithm is based on the application of Hankel matrices of variable size and can equally be considered as a generalized version of the KL method (GKL) or as a reduced version of the ML estimation. The methodology applied to derive the GKL algorithm is used to present a straightforward derivation of the subspace identification algorithm.