引用本文:邓自立.时域Wiener状态滤波新方法[J].控制理论与应用,2004,21(3):367~372.[点击复制]
DENG Zi-li.New approach to Wiener state filtering in time-domain[J].Control Theory and Technology,2004,21(3):367~372.[点击复制]
时域Wiener状态滤波新方法
New approach to Wiener state filtering in time-domain
摘要点击 1861  全文点击 1146  投稿时间：2002-09-18  修订日期：2003-05-26
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DOI编号  10.7641/j.issn.1000-8152.2004.3.009
  2004,21(3):367-372
中文关键词  随机系统  状态估计  Wiener滤波  Kalman滤波  时域方法
英文关键词  stochastic system  state estimation  Wiener filtering  Kalman filtering  time-domain approach
基金项目  国家自然科学基金项目(60374026); 黑龙江省自然科学基金项目(F01-15).
作者单位
邓自立 黑龙江大学 应用数学研究所 黑龙江大学自动化系，黑龙江哈尔滨 150080 
中文摘要
      基于稳态Kalman滤波器和射影理论,提出了统一和通用的时域Wiener状态滤波新方法,用它得到带非零均值相关噪声线性随机系统的渐近稳定的Wiener状态估值器和解耦Wiener状态估值器.它可统一处理状态滤波、预报和平滑问题.发现了Kalman滤波器和Wiener滤波器之间的变换关系,Wiener状态估值器可由Kalman估值器通过自回归滑动平均(ARMA)新息模型得到.一个仿真例子说明了其有效性.
英文摘要
      Based on the steady-state Kalman filter and projection theory,a new unified and general approach to the time-domain Wiener state filtering is presented,by which the asymptotically stable Wiener state estimator and decoupled Wiener state estimators are presented for linear stochastic systems with correlated noises having non-zero means.It can handle the state filtering,prediction and smoothing problems in a unified framework.The transformation relationship between the Kalman filters and Wiener filters is discovered,the Wiener state estimators can be obtained from the Kalman estimators by means of the autoregressive moving average (ARMA) innovation model.A simulation example shows its effectiveness.