Multi-Stage Unknown Input Filtering of Linear Systems

Abstract

In the paper, a practical unknown input filtering problem, whether or not the unknown input model is given, is explored. Through the existing various two-stage Kalman filters, a unified two-stage Kalman filter based on the previously proposed optimal two-stage Kalman filter is developed to implement different unknown input estimators. Moreover, a multiple unknown input model-based system transformation is proposed to transform the original system into an augmented state system. Then, a cost-effective multi-stage Kalman filter is developed to implement the augmented state filter within a parameterized unknown input model-based filtering approach, which is suitable for parallel computing. An illustrative example is given to show the effectiveness of the proposed results.