Unbiased minimum-variance linear state estimation
Automatica (Journal of IFAC)
Unbiased minimum variance estimation for systems with unknown exogenous inputs
Automatica (Journal of IFAC)
State and input estimation for a class of uncertain systems
Automatica (Journal of IFAC)
Decentralized Estimation and Control for Multisensor Systems
Decentralized Estimation and Control for Multisensor Systems
Brief paper: Unbiased minimum-variance input and state estimation for linear discrete-time systems
Automatica (Journal of IFAC)
Brief paper: Unbiased minimum-variance state estimation for linear systems with unknown input
Automatica (Journal of IFAC)
Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods
Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods
Optimal filtering for systems with unknown inputs via the descriptor Kalman filtering method
Automatica (Journal of IFAC)
State and input simultaneous estimation for a class of nonlinear systems
Automatica (Journal of IFAC)
Simultaneous state and input estimation of hybrid systems with unknown inputs
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
This paper studies the problem of simultaneous input and state estimation (SISE) for nonlinear dynamical systems with and without direct input-output feedthrough. We take a Bayesian perspective to develop a sequential joint input and state estimation approach. Our scheme gives rise to a nonlinear Maximum a Posteriori optimization problem, which we solve using the classical Gauss-Newton method. The proposed approach generalizes a number of SISE methods presented in the literature. We illustrate the effectiveness of the proposed scheme for nonlinear systems with direct feedthrough in an oceanographic flow field estimation problem involving submersible drogues that measure position intermittently and acceleration continuously.