Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Robust extended Kalman filtering
IEEE Transactions on Signal Processing
An Extended Robust H Filter for Nonlinear Constrained Uncertain Systems
IEEE Transactions on Signal Processing
Robust discrete-time minimum-variance filtering
IEEE Transactions on Signal Processing
Finite-horizon robust Kalman filter design
IEEE Transactions on Signal Processing
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This paper presents a robust filter for discrete-time nonlinear systems subject to uncertainties. The nonlinear functions are assumed to be uncertain but belonging to a conic region. This condition is characterized as a Lipschitz condition on the system state and control signal residuals. The proposed design also allows dynamic and measurement noises to have unknown time-varying expected values, covariances and cross-covariances. The filter furnishes estimations with the a priori and a posteriori variance errors bounded for all allowed uncertainties.