Multi-Step Delayed Input and State Estimation: A System Augmentation Approach

This paper presents a system augmentation-based unbiased minimum-variance input and state estimation for systems with unknown inputs which can be reconstructed with a multi-step delay. An estimable input generating model (EIGM)-based system augmented approach is proposed to facilitate the filter design. It is shown that, via this new filtering approach the optimal unknown input and state estimation can be simultaneously achieved through the previously proposed robust two-stage Kalman filter (RTSKF). An illustrative example is given to show the effectiveness of the proposed results.