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For the descriptor system with unknown noise variances, the self-tuning full-order Kalman filter with self-tuning descriptor Riccati equation is presented. Firstly, the fused estimates of these unknown noise variances can be obtained by the correlation function method. Then, substituting these fused estimates into the recursive Kalman filter with descriptor Riccati equation yields the self-tuning...
For the autoregressive (AR) signals with multisensor, unknown model parameters and unknown noise variances, using 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 Wiener filter based on the autoregressive...
For the multisensor multi-channel autoregressive moving average (ARMA) signals with unknown parameters and noise variances, using the modern time series analysis method, based on the on-line identification of the local ARMA innovation models and fused moving average (MA) innovation model, a class of self-tuning weighted measurement fusion filter and smoother are presented. By using the dynamic error...
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