Chenjian Ran

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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)
For multisensor multi-channel autoregressive moving average(ARMA) signal with white measurement noises and a common disturbance measurement noise, when the model parameters and the noise variances are all unknown, an information fusion multi-stage identification method is presented. It consists of three stages: In the first stage, the local and fused(More)
For the multisensor systems with same measurement matrix, when the noise variances are unknown, an information fusion noise variance estimator is presented using the correlation method and least squares fusion criterion. It has the consistence and reliability of accuracy. Further, a self-tuning weighted measurement fusion Kalman filter based on the(More)