Chenjian Ran

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For the multi-sensor systems with correlated input and measurement noises, under the Linear Unbiased Minimum Variance criterion, the centralized and the weighted measurement fusion structures are derived. Applying the left-coprime factorization algorithm based on modern time series analysis method, the fused ARMA innovation models are obtained, and then by(More)
For the multisensor linear stochastic descriptor system, the information fusion full-order descriptor Kalman filters are presented, which are different from the reduced-order Kalman filtering algorithms and can improve filtering accuracy. The centralized fusion full-order descriptor Kalman filter can obtain the globally optimal filter, by extending all(More)
In order to obtain more accurate state estimation from multisensor system, the state estimation with two-level fusion structure is presented. By means of measurement fusion algorithm, the local first-level fusion centers can obtain the globally optimal fused measurement information, and then the local state estimation can be got by classical Kalman(More)