Identification of Partially Unknown System Matrix of Discrete-time Stochastic Systems via Pseudomeasurement Approach
Akio Tanikawa, Yuichi Sawada
In this paper, a new identification method of linear discrete-time 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 was first introduced by Whitecombe in tracking of maneuvering targets for obtaining high-accurate estimates of the system states from noisy observation data and has been used by several researchers for various purposes. We utilize the pseudomeasurement as a ficticious observation process on the unknown entries of the system matrix for obtaining better estimates of them. Augmenting the pseudomeasurement with the original observation process, we derive the new identification method by applying the extended Kalman filter.