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For the multisensor linear stochastic descriptor systems, the local reduced-order Kalman estimators and their estimation error variances are presented, based on the Kalman filtering theory and the singular value decomposition (SVD) method. Then, covariance intersection (CI) fusion reduced-order Kalman estimator and its estimation error variance are obtained fusing these local estimators. Compared...
For the multisensor linear stochastic descriptor system, the information fusion full-order descriptor Kalman filters are presented, which are different from the reduced-order Kalman filtering algorithms and can improve filtering accuracy. The centralized fusion full-order descriptor Kalman filter can obtain the globally optimal filter, by extending all measurement information. The weighted measurement...
For the multi-sensor systems with correlated input and measurement noises, under the Linear Unbiased Minimum Variance criterion, the centralized and the weighted measurement fusion structures are derived. Applying the left-coprime factorization algorithm based on modern time series analysis method, the fused ARMA innovation models are obtained, and then by the universal Wiener estimators, the corresponding...
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