Kalman filtering-based information fusion Wiener filter of autoregressive moving average signals
基于Kalman滤波的自回归滑动平均信号信息融合Wiener滤波器

By using the Kalman filtering method and the linear minimum variance optimal fusion rule weighted by matrices,a multisensor information fusion Wiener filter is presented for the multichannel autoregressive moving average(ARMA) signals with white observation noise.It can handle the information fusion filtering,smoothing and prediction problems in a unified framework.In order to compute the optimal weighting matrices,the formula of computing the cross-covariance matrices among local filtering errors,is presented.Compared with the single sensor case,the estimation accuracy is improved.A simulation example for a target tracking system with three-sensor shows its effectiveness.