Self-tuning weighted measurement fusion Wiener signal filter

For the multisensor single channel autoregressive moving average (ARMA) signals with unknown model parameters and noise variances, using the recursive instrumental variable (RIV) and the correlated method, the strong consistent information fusion estimators of model parameters and noise variances are presented, and then substituting them into the optimal weighted measurement fusion Wiener signal filter, a self-tuning weighted measurement fusion Wiener signal filter is presented. Further, applying the dynamic error system analysis (DESA) 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 asymptotically global optimality. A simulation example shows its effectiveness.