Steady-state Kalman filtering with an H∞ error bound
Systems & Control Letters
H∞ filtering for linear periodic systems with parameter uncertainty
Systems & Control Letters
Robust control of discrete time uncertain dynamical systems
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Robust Kalman filtering for uncertain systems
Systems & Control Letters
Game theory approach to discrete H∞ filter design
IEEE Transactions on Signal Processing
A game theory approach to robust discrete-timeH∞-estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Robust minimum variance filtering
IEEE Transactions on Signal Processing
Robust discrete-time minimum-variance filtering
IEEE Transactions on Signal Processing
Robust filtering with randomly varying sensor delay: the finite-horizon case
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust H∞filtering with error variance constraints on GPS/INS integrated navigation systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Robust H-infinity filtering on uncertain time-delay systems under sampled measurements
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Comparison of sensor fusion methods for an SMA-based hexapod biomimetic robot
Robotics and Autonomous Systems
IEEE Transactions on Signal Processing
Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
Journal of Control Science and Engineering - Special issue on Advances in Methods for Control over Networks
Network-based H∞ filtering using a logic jumping-like trigger
Automatica (Journal of IFAC)
Hi-index | 22.16 |
The robust H"~ filtering problem with error variance constraints is considered for discrete time-varying systems subject to norm-bounded parameter uncertainties in both the state and the output matrices of the state-space model. Sufficient conditions for a finite-horizon filter to satisfy state estimation error variance constraints as well as prescribed H"~ performance for all admissible perturbations are given in terms of two discrete Riccati difference equations, which are of a form suitable for recursive computation. The results are extended to cover the case of stationary filtering over an infinite-horizon for uncertain time-invariant systems.