Self-tuning information fusion wiener filter for the AR signals and its convergence

For the multisensor autoregressive (AR) signals with unknown model parameters and noise variances, using recursive instrumental variable (RIV) algorithm, the correlation function method and the Gevers-Wouters algorithm with dead band, the information fusion estimators of model parameters and noise variances are presented. They have strong consistence. Then substituting them into the optimal fusion signal filter weighted by scalars, a self-tuning information fusion Wiener filter for the AR signals is presented. Further, applying the dynamic error system analysis method, it is rigorously proved that the self-tuning fused Wiener filter converges to the optimal fused Wiener filter in a realization, so that it has asymptotic optimality. A simulation example applied to signal processing shows its effectiveness.