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Gui-huan Nie
Heilongjiang University

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Robust Weighted Measurement Fusion Kalman Predictors with Uncertain Noise Variances Wen-juan Qi; Peng Zhang; Gui-huan Nie; Zi-li Deng
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

For the multisensor system with uncertain noise variances, using the minimax robust estimation principle, the local and weighted measurement fusion robust time-varying Kalman predictors are presented based on the worst-case conservative system with the conservative upper bound of noise variances. The actual prediction error variances are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov approach is proposed for the robustness analysis and their robust accuracy relations are proved. It is proved that the robust accuracy of weighted measurement robust fuser is higher than that of each local robust Kalman predictor. Specially, the corresponding steady-state robust local and weighted measurement fusion Kalman predictors are also proposed and the convergence in a realization between time-varying and steady-state Kalman predictors is proved by the dynamic error system analysis (DESA) method. A Monte-Carlo simulation example shows the effectiveness of the robustness and accuracy relations. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5453
Co-Authors Peng Zhang Wen-juan Qi Zi-li Deng