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 ,  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.