A Discrete-Time Robust Extended Kalman Filter for Uncertain Systems With Sum Quadratic Constraints

This technical note outlines the formulation of a novel discrete-time robust extended Kalman filter for uncertain systems with uncertainties described in terms of sum quadratic constraints. The robust filter is an approximate set-valued state estimator which is robust in the sense that it can handle...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2009-04, Vol.54 (4), p.850-854
Main Authors: Kallapur, A.G., Petersen, I.R., Anavatti, S.G.
Format: Article
Language:English
Subjects:
Applied sciences
Approximation
Australia
Computer science
control theory
systems
Control theory. Systems
Difference equations
Dynamic programming
Estimators
Exact sciences and technology
Extended Kalman filter
Extended Kalman filter (EKF)
Filters
Handles
integral quadratic constant (IQC)
Mathematical models
Modelling and identification
Noise robustness
Nonlinear dynamical systems
Nonlinear systems
Optimal control
State estimation
sum quadratic constant (SQC)
Uncertain systems
Uncertainty
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Summary:This technical note outlines the formulation of a novel discrete-time robust extended Kalman filter for uncertain systems with uncertainties described in terms of sum quadratic constraints. The robust filter is an approximate set-valued state estimator which is robust in the sense that it can handle modeling uncertainties in addition to exogenous noise. Riccati and filter difference equations are obtained as an approximate solution to a reverse-time optimal control problem defining the set-valued state estimator. In order to obtain a solution to the set-valued state estimation problem, the discrete-time system dynamics are modeled backwards in time.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2008.2010962