Robust steady-state filtering for systems with deterministic and stochastic uncertainties
Authors:
- 23 September 2003
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 51 (10) , 2550-2558
Abstract
For uncertain systems containing both deterministic and stochastic uncertainties, we consider two problems of optimal filtering. The first is the design of a linear time-invariant filter that minimizes an upper bound on the mean energy gain between the noise affecting the system and the estimation error. The second is the design of a linear time-invariant filter that minimizes an upper bound on the asymptotic mean square estimation error when the plant is driven by a white noise. We present filtering algorithms that solve each of these problems, with the filter parameters determined via convex optimization based on linear matrix inequalities. We demonstrate the performance of these robust algorithms on a numerical example consisting of the design of equalizers for a communication channel.Keywords
This publication has 21 references indexed in Scilit:
- Robust and reduced-order filtering: new LMI-based characterizations and methodsIEEE Transactions on Signal Processing, 2001
- New approaches to robust minimum variance filter designIEEE Transactions on Signal Processing, 2001
- On linear H/sup ∞/ equalization of communication channelsIEEE Transactions on Signal Processing, 2000
- Optimal reception, performance bound, and cutoff rate analysis of references-assisted coherent CDMA communications with applicationsIEEE Transactions on Communications, 1999
- Optimal linear filtering under parameter uncertaintyIEEE Transactions on Signal Processing, 1999
- Robust state estimation and model validation for discrete-time uncertain systems with a deterministic description of noise and uncertaintyAutomatica, 1998
- A linear matrix inequality approach to robust H/sub ∞/ filteringIEEE Transactions on Signal Processing, 1997
- Robust Kalman filtering for uncertain discrete-time systemsIEEE Transactions on Automatic Control, 1994
- Gain scheduling via linear fractional transformationsSystems & Control Letters, 1994
- Minimax robust deconvolution filters under stochastic parametric and noise uncertaintiesIEEE Transactions on Signal Processing, 1994