Self-tuning information fusion Wiener predictor and its convergence
自校正信息融合Wiener预报器及其收敛性

For the multisensor systems with correlated measurement noises and unknown noise statistics.the on-line noise statistics estimators are obtained by the correlation method. Under the linear minimum variance optimal information fusion criterion weighted by scalars for components,by the modem time series analysis method.a self-tuning decoupled fusion Wiener predictor is presented based on the identification of the moving average(MA) innovation models. By using the dynamic error system analysis(DESA) method,it is proved that the self-tuning fusion Wiener predictor converges to the optimal fusion Wiener predictor,so that it has the asymptotic optimality. Its accuracy is higher than that of each local self-tuning Wiener predictor. Its algorithm is simple.and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.