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Based on the decomposition in canonical form, an optimal state filter in the linear unbiased minimum variance sense is given for single-sensor stochastic singular systems with unknown disturbance and correlated noises in the case of Y-observable system, which is independent of the unknown disturbance. When the system is measured by multiple sensors, the computation formula for the filtering error...
An unbiased state filter in linear minimum variance sense is developed for discrete-time stochastic linear systems with unknown inputs and correlated noises, where there is not any prior information for the unknown inputs. When there are multiple sensors, the cross-covariance matrix of filtering errors between any two sensors is derived. Further, the distributed scalar-weighted fusion state filter...
Based on the innovation analysis approach, the estimation error cross-covariance matrices between local estimators based on any two sensors are derived for multi-sensor multi-delay systems with correlated noises. The non-augmented distributed weighted fusion optimal estimators are given based on the optimal weighted fusion estimation algorithm in the linear minimum variance sense. Compared with the...
The filtering fusion problem of a descriptor system with delayed measurements is transferred to the different-step prediction fusion problem of two reduced-order normal subsystems without delayed measurements and with correlated noises. Using projection theory, the cross-covariance matrix of different-step prediction errors between any two sensor subsystems is derived. Based on the fusion algorithm...
This paper is concerned with distributed fusion estimation for discrete-time stochastic linear systems with multiple sensors having multiple time delayed measurements. A distributed weighted fusion suboptimal Kalman filter is given based on the local suboptimal Kalman filters and the optimal fusion algorithm weighted by matrices in the linear minimum variance sense. Compared with the augmented Kalman...
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