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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 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 statistics and with the same measurement matrices, using the weighted least squares method, an equivalent fused measurement equation with unknown noise variance is obtained, by the modern time series analysis method, based on on-line identification of the moving average (MA) innovation model parameters, the estimators of noise statistics are obtained,...
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