A finite-horizon adaptive Kalman filter for linear systems with unknown disturbances
Signal Processing - Signal processing in communications
Automatica (Journal of IFAC)
H2 robust filter design with performance certificate via convex programming
Automatica (Journal of IFAC)
Robust filtering with randomly varying sensor delay: the finite-horizon case
IEEE Transactions on Circuits and Systems Part I: Regular Papers
ACC'09 Proceedings of the 2009 conference on American Control Conference
Brief Design and analysis of discrete-time robust Kalman filters
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Unified forms for Kalman and finite impulse response filtering and smoothing
Automatica (Journal of IFAC)
Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia
Computers in Biology and Medicine
International Journal of Sensor Networks
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The bounded-variance filtered estimation of the state of an uncertain, linear, discrete-time system, with an unknown norm-bounded parameter matrix, is considered. An upper bound on the variance of the estimation error is found for all admissible systems, and estimators are derived that minimize the latter bound. We treat the finite-horizon, time-varying case and the infinite-time case, where the nominal system model is time invariant. In the special stationary case, where it is known that the uncertain system is time invariant, we provide a robust filter for all uncertainties that still keep the system asymptotically stable