Gui-Li Tao

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For the linear discrete time-invariant stochastic systems with unknown model parameters and noise variances, substituting their online consistent estimators into the steady-state optimal Riccati equation, a self-tuning Riccati equation is presented. By the dynamic variance error system analysis (DVESA) method, it is proved that the self-tuning Riccati(More)
For the single channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the fused estimators of model parameters and noise variances can be obtained by the recursive instrumental variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band. They have the(More)
For the multisensor multi-channel autoregressive moving average (ARMA) signals with white measurement noises and an AR colored measurement noise as common disturbance noises, a multi-stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise(More)
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