Identification of Unknown Exogenous Input of Stochastic Linear Systems via Pseudomeasurement Approach

Abstract

A joint method is proposed for estimating the systems state and identifying the unknown (stepwise) exogenous input to the stochastic dynamical systems by introducing the idea of pseudomeasurement. Both state estimation and parameter identification are processed by the Kalman filter, incorporating the pseudomeasurements. An application of the method to the change detection is also discussed briefly. The proposed method is tested through simulation studies.