The self-tuning distributed information fusion wiener filter for the ARMA signals

Abstract

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 consistency. The optimal distributed fusion Wiener signal filter is obtained by weighting the local optimal Wiener filters. Substituting the fused estimators into optimal distributed fusion Wiener filter, a self-tuning distributed fusion Wiener filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning distributed fusion Wiener filter converges to the optimal distributed fusion Wiener filter, so that it has asymptotic optimality. Its accuracy is higher that of each local self-tuning Wiener filter. A simulation example shows it effectiveness.