Abstract
In this paper, a new identification method of discrete-time linear stochastic systems is proposed. We assume that some entries of the system matrix are unknown and propose a new method which identifies those unknown entries and the state vector of the system simultaneously. The key idea of the proposed method is the use of pseudomeasurement which is a fictitious observation process on the unknown entries and has been introduced by Kameyama and Ohsumi for continuous-time linear stochastic systems. Augmenting the pseudomeasurement with the original observation process, we derive the new identification method by applying the extended Kalman filter.
