Combining of Local Estimates with Application to Identification and Filtering Problems

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.

Important Links:

Go Back