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For the multisensor systems with companion form and unknown model parameters and noise variances, using recursive instrumental variable(RIV) algorithm, the local and fused model parameter estimators are obtained. Based on the fused model parameter estimators, the information fusion noise variance estimators are presented by using correlation method. They have strong consistence. Further, a self-tuning...
For multisensor discrete time-invariant systems with the companion form, and unknown model parameters and noise variances, based on the recursive extended least square (RELS) and the correlation method, the strong consistent information fusion estimators of model parameters and noise variances are presented, and then by substituting them into the optimal weighted measurement fusion Kalman filter based...
For the multisensor systems with unknown noise variances, using correlation method and least squares fusion criterion, information fusion noise variance estimators are presented by the average of local noise variance estimators, which have the consistence. Substituting the fused noise variance online estimators into the optimal Riccati equation and the optimal weighted measurement fusion Kalman filter,...
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 information matrix is presented. By using the dynamic...
For the multisensor systems with unknown noise variances, by the correlation method, the information fusion noise variance estimators are presented by taking the average of the local noise variance estimators under the least squares fusion rule. They have the average accuracy and have consistency. A self-tuning Riccati equation with the fused noise variance estimators is presented, and then a self-tuning...
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