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This paper is concerned with the distributed fusion filtering problem for a class of asynchronous multi-rate multi-sensor systems with different packet dropout rates, where the system is described at the highest sampling rate and different sensors may have different measurement sampling rates. Firstly, the multi-rate fusion estimation problem is transformed into an equivalent single rate fusion estimation...
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...
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...
Based on the optimal fusion algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion reduced-order Kalman filter with scalar weights is presented for discrete-time stochastic singular systems with multiple sensors and correlated noises. It has higher accuracy than any local filter does. Compared with the distributed fusion filter weighted by matrices, it has...
Using correlation functions, a distributed identification approach for noise statistic information is given for stochastic singular systems measured by multiple sensors with unknown noise statistics information. Compared with the centralized identification method, the computation burden can be reduced. Further, a self-tuning information fusion reduced-order Kalman predictor with a two-stage fusion...
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