%0 Journal Article
%T Self-tuning Distributed Measurement Fusion Kalman Filter<br>自校正分布式观测融合Kalman滤波器
%A Deng Zi-li
%A Hao Gang
%A <br>邓自立
%A 郝钢
%J 电子与信息学报
%D 2007
%I 
%X For the multisensor system with unknown noise statistics, and with the measurement matrices having a same right factor, based on Weighted Least Squares(WLS) method, an equivalent fusion measurement equation is obtained. Using the modern time series analysis method, based on on-line identification of the innovation model parameters, unknown noise variances can be estimated, and a self-tuning weighted measurement fusion Kalman filter is presented. Under the assumptions that the parameter estimation of the innovation model is consistent and the measurement data are bounded, it is proved that the self-tuning Kalman filter converges to globally optimal fusion Kalman filter with known noise statistics, so that it has asymptotic global optimality. A simulation example for a tracking system with 4-sensor shows its effectiveness.
%K Multisensor information fusion
%K Distributed measurement fusion
%K Self-tuning Kalman filters
%K Noise variance estimation
%K Modern time series analysis method<br>多传感器信息融合
%K 分布式观测融合
%K 自校正Kalman滤波器
%K 噪声方差估计
%K 现代时间序列分析方法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=8288A0C08A18AE93&yid=A732AF04DDA03BB3&vid=771469D9D58C34FF&iid=5D311CA918CA9A03&sid=EFD9B70C3A0525B7&eid=0ADDA70032243493&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=9