Comparative study of state and unknown input estimation for continuous–discrete​ stochastic systems - Peng Lu (Franklin Open, 2022)
Joint state and unknown input estimation for continuous–discrete stochastic systems can be classified into two types: with and without modeling of unknown inputs. This paper first proposes a recursive four-step Kalman filter for addressing the joint state and unknown input estimation of continuous–discrete stochastic systems and presents its stability conditions. Then, a theoretical analysis of the performances using the unknown input Kalman filters and an adaptive augmented Kalman filter is presented and validated using numerical simulations under different situations. Both the theory and simulation demonstrate that the adaptive augmented Kalman filters is a better option when addressing unknown inputs for continuous–discrete stochastic systems.