%0 Journal Article
%T Kalman filtering-based information fusion Wiener filter of autoregressive moving average signals<br>基于Kalman滤波的自回归滑动平均信号信息融合Wiener滤波器
%A DENG Zi-li
%A GAO Yuan
%A <br>邓自立
%A 高媛
%J 控制理论与应用
%D 2005
%I 
%X 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.
%K multichannel AMAR signal
%K multisensor information fusion
%K optimal fusion rule weighted by matrices
%K Wiener filter
%K Kalman filtering method<br>多通道ARMA信号
%K 多传感器信息融合
%K 按矩阵加权最优融合规则
%K Wiener滤波器
%K Kalman滤波方法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=54A48FF7FC5FDB6B&yid=2DD7160C83D0ACED&vid=BC12EA701C895178&iid=E158A972A605785F&sid=9A596D09E9486F3E&eid=0636354D8CF77519&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=7