WMF reduced-order robust estimators for multisensor descriptor systems

WMF reduced-order robust estimators for multisensor descriptor systems

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For multisensor descriptor systems with uncertain noise variances, the weighted measurement fusion (WMF) reduced-order robust estimators are presented. The multisensor descriptor systems with known upper bounds of uncertain noise variances are defined as the worst-case conservative multisensor descriptor systems. For the worst-case conservative multisensor descriptor systems, the WMF reduced-order conservative Kalman estimators, including filter, predictor and smoother, are obtained, by applying the WMF method and the singular value decomposition method. However, the measurements of the conservative multisensor descriptor systems are unavailable. Replacing these unavailable conservative measurements by the actual and available measurements yields the actual WMF reduced-order robust estimators. Their robustness is proven by the dynamic error variance system method, i.e. the actual estimation error variances are not greater than those of conservative systems. Two simulation examples of four-sensor descriptor systems show the effectiveness of the presented algorithms.

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