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This paper considers the problem of system identification for linear timevarying systems. We propose a new system realization approach that uses an"information-state" as the state vector, where the "information-state" iscomposed of a finite number of past inputs and outputs. The systemidentification algorithm uses input-output data to fit an autoregressive movingaverage model (ARMA) to represent the current output in terms of finite pastinputs and outputs. This information-state-based approach allows us to directlyrealize a state-space model using the estimated time varying ARMA paramterslinear time varying (LTV) systems. The paper develops the theoreticalfoundation for using ARMA parameters-based system representation using only theconcept of linear observability, details the reasoning for exact outputmodeling using only the finite history, and shows that there is no need toseparate the free and the forced response for identification. The paper alsodiscusses the implications of using the information-state system for optimaloutput feedback control and shows that the solution obtained using a suitablyposed information state problem is optimal for the original problem. Theproposed approach is tested on various different systems, and the performanceis compared with state-of-the-art LTV system identification techniques.

An Information-State Based Approach to Linear Time Varying System Identification and Control