A finite-horizon adaptive Kalman filter for linear systems with unknown disturbances
Signal Processing - Signal processing in communications
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
Robust switching-type H∞filtering for time-varying uncertain time-delay systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Robust switching-type H∞ filter design for linear uncertain systems with time-varying delay
Information Sciences: an International Journal
Robust H2 and H∞ filtering for uncertain linear systems
Automatica (Journal of IFAC)
Filter design for LPV systems using quadratically parameter-dependent Lyapunov functions
Automatica (Journal of IFAC)
A robust H? filtering approach for singular systems
International Journal of Systems, Control and Communications
Technical Communique: Dynamic observers for linear time-invariant systems
Automatica (Journal of IFAC)
Brief Explicit formulas for LMI-based H2 filtering and deconvolution
Automatica (Journal of IFAC)
Technical Communique: Resilient linear filtering of uncertain systems
Automatica (Journal of IFAC)
Robust output-feedback controller design via local BMI optimization
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
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This paper addresses the problem of designing a guaranteed minimum error variance robust filter for convex bounded parameter uncertainty in the state, output, and input matrices. The design procedure is valid for linear filters that are obtained from the minimization of an upper bound of the error variance holding for all admissible parameter uncertainty. The results provided generalize the ones available in the literature to date in several directions. First, all system matrices can be corrupted by parameter uncertainty, and the admissible uncertainty may be structured. Assuming the order of the uncertain system is known, the optimal robust linear filter is proved to be of the same order as the order of the system. In the present case of convex bounded parameter uncertainty, the basic numerical design tools are linear matrix inequality (LMI) solvers instead of the Riccati equation solvers used for the design of robust filters available in the literature to date. The paper that contains the most important and very recent results on robust filtering is used for comparison purposes. In particular, it is shown that under the same assumptions, our results are generally better as far as the minimization of a guaranteed error variance is considered. Some numerical examples illustrate the theoretical results