V. Shin and J.I. Ahn (Korea)
Data fusion, Estimation, Identification, Detection, Kalman filter, Adaptive filter
This paper considers joint identification and estimation problem for linear discrete-time systems with uncertainties. In [1], [2] we have proposed the fusion formula (FF) for an arbitrary number of correlated and uncorrelated estimates. In this paper the FF is applied to detection and filtering problem. The new reduced-order suboptimal filter with parallel structure is herein proposed. In consequence of parallel structure of the proposed filter parallel computers can be used for their design. A lower computational complexity and lower memory demand are achieved with the proposed filter than in the optimal adaptive Lainiotis-Kalman filter. Example demonstrates the accuracy of the new filter.
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