%0 Journal Article
%T Improved particle filter in data assimilation<br>基于粒子滤波的一种改进的资料同化方法
%A Leng Hong-Ze
%A Song Jun-Qiang
%A Cao Xiao-Qun
%A Yang Jin-Hui
%A <br>冷洪泽
%A 宋君强
%A 曹小群
%A 杨锦辉
%J 物理学报
%D 2012
%I 
%X Owing to the fact that standard particle filter and ensemble Kalman filter can not efficiently represent the posterior probability density function (PDF), an improved particle filter is proposed. In this algorithm, an innovation step is introduced after the prediction step, and the analyses of non-observation time and observation time are treated separately. The numerical simulations of a low- and a high-dimensional systems show that this new particle filter can follow the true state of a highly nonlinear non-Gaussian system very well.
%K assimilation
%K nonlinear
%K particle filter
%K ensemble Kalman filter<br>同化
%K 非线性
%K 粒子滤波
%K 集合卡尔曼滤波
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=78F360FC29C479113400CB7E748D6004&yid=99E9153A83D4CB11&vid=1D0FA33DA02ABACD&iid=DF92D298D3FF1E6E&sid=9B6A0E60ECE08D80&eid=4029DE9862CF3DA2&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=26