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- W2024148282 abstract "For multisensor systems with unknown parameters and noise variances, three self-tuning measurement fusion Kalman predictors based on the information matrix equation are presented by substituting the online estimators of unknown parameters and noise variances into the optimal measurement fusion steady-state Kalman predictors. By the dynamic variance error system analysis method, the convergence of the self-tuning information matrix equation is proved. Further, it is proved by the dynamic error system analysis method that the proposed self-tuning measurement fusion Kalman predictors converge to the optimal measurement fusion steady-state Kalman predictors in a realisation, so they have asymptotical global optimality. Compared with the centralised measurement fusion Kalman predictors based on the Riccati equation, they can significantly reduce the computational burden. A simulation example applied to signal processing shows their effectiveness." @default.
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- W2024148282 date "2011-10-01" @default.
- W2024148282 modified "2023-09-24" @default.
- W2024148282 title "Self-tuning measurement fusion Kalman predictors and their convergence analysis" @default.
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- W2024148282 doi "https://doi.org/10.1080/00207721003646223" @default.
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