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For the multisensor systems with uncertain-variance linearly correlated white noises, according to the minimax robust estimation principle, applying the weighted least squares(WLS) and the full-rank decomposition of matrix, the robust centralized fusion and weighted measurement fusion steady-state Kalman estimators (filter, predictor and smoother) are presented in a unified framework. Their equivalence...
In this work, guaranteed cost robust centralized fusion (CF) and weighted measurement fusion (WMF) steady-state Kalman filters problems in multisensor system with uncertain noise variances are addressed. By parameterzing the perturbations of uncertain noise variances, two classes of guaranteed cost robust CF and WMF Kalman filters are proposed based on minimax robust estimation principle (MREP). The...
In this paper, the robust centralized fusion Kalman prediction problem is considered for linear discrete-time multisensor systems with multiplicative noises, uncertain noise variances and missing measurements. By introducing two fictitious noises, the system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case centralized...
For the multisensor systems with unknown model parameters and noise variances, based on the system identification algorithm and correlation method, the estimators of model parameters and noise variances can be obtained. Based on the information matrix, a self-tuning centralized fusion Wiener filter is presented by substituting the estimators into the corresponding optimal filter. Using the dynamic...
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