MAXIMUM LIKELIHOOD IDENTIFICATION OF STATE SPACE MODELS FOR LINEAR DYNAMIC SYSTEMS

Maximum likelihood (ML) identification of state space models for linear dynamic systems is presented in a unified tutorial form. First linear filtering theory and classical maximum likelihood theory are reviewed. Then ML identification of linear state space models is discussed. A compact user-oriented presentation of results scattered in the literature is given for computing the likelihood function, maximizing it, evaluating the Fisher information matrix and finding the asymptotic properties of ML parameter estimates. The practically important case where a system is described by a simpler model is also briefly discussed.

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