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This paper studies the linear filtering problem in the presence of multiple packet dropouts and time-correlated channel noise that obeys a linear system model with white noise. By applying the measurement differencing method, the time-correlated channel noise is removed and a new measurement model with white noise is established. Then based on the new model, a linear minimum variance (LMV) filter...
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 design problem of multi-sensor optimal H∞ fusion controller for a class of discrete-time systems with missing measurements. Based on Lyapunov theory and linear matrix inequality technology, the centralized and distributed fusion controllers are designed, respectively such that, for all possible missing measurements, the closed-loop systems are asymptotically stable...
This paper is concerned with the optimal white noise estimation problem for linear discrete-time stochastic systems with multiple packet dropouts. Based on the optimal linear state predictor, the optimal linear white noise filters for the input white noise and measurement white noise are developed in the linear minimum variance sense via the innovation analysis approach. When there are packet dropouts,...
For the time-variant multisensor systems with correlated measurement noises and different measurement matrices, on the basis of recursive least squares (RLS) method, least squares(LS) method and Kalman filtering theory, two information fusion Kalman filters are put forward. The theory is that firstly Cholesky factorization is used to convert the former multisensor systems into noise irrelative and...
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...
A new multi-sensor optimal information fusion criterion weighted by covariance is presented in the linear minimum variance sense. Based on this optimal fusion criterion, using the measurement white noise filters, a general multi-sensor optimal information fusion distributed Kalman filter is given for the discrete multichannel ARMA (autoregressive moving average) signals with correlated noises. It...
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