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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 system with different measurement matrices, correlated measurement noises and unknown noise variances, by correlated method, the online identifiers of the noise variances are obtained. Based on ARMA innovation model, a self-tuning weighted measurement fusion Kalman filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable...
By the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimation theory, a new local descriptor Wiener state estimators are presented, and using the optimal fusion rule weighted by diagonal matrices, a distributed decoupled descriptor Wiener state fuser is presented for the linear discrete stochastic descriptor systems with multisensor...
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...
By the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimation theory, using the optimal fusion rule weighted by scalars for components, a distributed descriptor Wiener state fuser is presented for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components...
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