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In this paper, an optimal distributed fusion estimation algorithm is presented for multi-sensor non-uniform sampling systems with the correlated process noise and measurement noises at the same time and network-induced packet dropouts. First, the non-uniform sampling problem is first transformed to a single-rate synchronous sampling problem. Then, local optimal filters and the filtering error cross-covariance...
This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor non-uniform sampling systems with correlated noises and fading measurements. The state is updated uniformly and the sensors sample measurement data randomly. The process noise and different measurement noises are correlated at the same instant. Moreover, the fading measurement phenomena may occur in...
This paper is concerned with the distributed fusion estimation problem for discrete-time stochastic linear system with multiple sensors having multiple delayed measurements and correlated noise. Distributed weighted fusion optimal estimators are given based on local optimal estimators from single sensor and the optimal scalar-weighted fusion algorithm in the linear minimum variance sense. Compared...
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...
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