Unbiased minimum-variance linear state estimation
Automatica (Journal of IFAC)
Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems: theory and application
Unbiased minimum variance estimation for systems with unknown exogenous inputs
Automatica (Journal of IFAC)
Reduced order Kalman filtering without model reduction
Control and Intelligent Systems
| Hi-index | 22.14 |
This paper presents an optimal reduced-order Kalman filter for discrete-time dynamic stochastic linear systems with unknown inputs. The problem is to estimate a part of the state vector in the case where none of the observations are assumed to be noise-free. The proposed filter is obtained by minimizing the trace of the estimation error covariance matrix with respect to the remaining degrees of freedom after noninteresting state and unknown inputs decoupling. The necessary and sufficient conditions for stability and convergence of the filter are established.