Topics in matrix analysis
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
An LMI approach to discrete-time observer design with stochastic resilience
Journal of Computational and Applied Mathematics
Robust discrete-time minimum-variance filtering
IEEE Transactions on Signal Processing
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters
Automatica (Journal of IFAC)
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
Automatica (Journal of IFAC)
Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
IEEE Transactions on Signal Processing
International Journal of Systems Science
Derivation of centralized and distributed filters using covariance information
Computational Statistics & Data Analysis
Brief paper: H∞ filtering with randomly occurring sensor saturations and missing measurements
Automatica (Journal of IFAC)
Optimal linear estimation for networked systems with communication constraints
Automatica (Journal of IFAC)
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Event-triggering in networked systems with probabilistic sensor and actuator faults
Information Sciences: an International Journal
Hi-index | 22.16 |
Linear minimum variance unbiased state estimation is considered for systems with uncertain parameters in their state space models and sensor failures. The existing results are generalized to the case where each sensor may fail at any sample time independently of the others. For robust performance, stochastic parameter perturbations are included in the system matrix. Also, stochastic perturbations are allowed in the estimator gain to guarantee resilient operation. An illustrative example is included to demonstrate performance improvement over the Kalman filter which does not include sensor failures in its measurement model.