%0 Journal Article
%T Self-tuning information fusion Kalmansmoother<br>自校正信息融合Kalman平滑器
%A Deng Zi-li
%A LI Chun-bo
%A <br>邓自立
%A 李春波
%J 控制理论与应用
%D 2007
%I 
%X For the multisensor systems with unknown noise statistics,using the modern time series analysis method, based on the on-line identification of the moving average (MA) innovation models,and based on the solution of the matrix equations for correlation function,the on-line estimators of noise statistics are obtained.Furthermore,under the linear minimum variance optimal information fusion criterion weighted by matrices,a self-tuning information fusion Kalman smoother is presented.A new concept of the convergence in a realization is presented,and it is proved that the self-tuning Kalman fuser converges to the optimal Kalman fuser in a realization,so that it has the asymptotic optimality.Compared with the single-sensor self-tuning Kalman smoother,its accuracy is improved.A simulation example for a target tracking system shows its effectiveness.
%K multisensor information fusion
%K weighted fusion
%K MA innovation model
%K system identification
%K noise variance estimation
%K self-tuning Kalman smoother<br>多传感器信息融合
%K 加权融合
%K MA新息模型
%K 系统辨识
%K 噪声方差估计
%K 自校正Kalman平滑器
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=9ECD2277F5A79A9E89CE0499FBD96F5B&yid=A732AF04DDA03BB3&vid=B91E8C6D6FE990DB&iid=0B39A22176CE99FB&sid=FBCA02DBD05BD4EA&eid=0C191C6ECF79047F&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=7