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Robust extended Kalman filtering for nonlinear systems with multiplicative noises. (English) Zbl 1213.93192
Summary: We investigate the robust filter design problem for nonlinear systems with multiplicative noises. The aim of the problem is to design a state estimator with a predictor-corrector structure, such that the upper bound on the state estimation error variance is minimized. A Robust Extended Kalman Filter (REKF) is proposed based on a novel method to obtain the upper bound on the variances of the multiplicative noises. Further analysis shows that the proposed filter guarantees a bounded energy gain from the multiplicative noises to the estimation error. The REKF is implemented on the satellite attitude determination system that consists of the gyroscopes and the star sensors. Its performance is illustrated by using the real data obtained from a gyroscope. Simulation results show that the REKF outperforms another robust algorithm.

MSC:
93E11 Filtering in stochastic control theory
93C10 Nonlinear systems in control theory
93E10 Estimation and detection in stochastic control theory
93C95 Application models in control theory
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