基于Kalman滤波的Wiener状态估值器(英文)  

Wiener State Estimators Based on Kalman Filtering

作　　者：邓自立[1] 孙书利[2] 

机构地区：[1]黑龙江大学应用数学研究所,哈尔滨150080 [2]黑龙江大学自动化系,哈尔滨150080

出　　处：《自动化学报》2004年第1期126-130,共5页Acta Automatica Sinica

基　　金：National Natural Science Foundation of P. R. China (69774019) ;by Natural Science Foundation of Heilongjiang Province (F01-15)

摘　　要：应用经典稳态Kalman滤波理论提出了设计Wiener状态估值器的新方法,其原理是:基于在Wiener滤波器形式下的稳态Kalman滤波器和预报器及ARMA新息模型,由稳态最优非递推状态估值器的递推变形引出Wiener状态估值器.所提出的Wiener状态估值器可统一处理状态滤波、预报和平滑问题.它们具有ARMA递推形式,且具有渐近稳定性和最优性,仿真例子说明了它们的有效性.Using classical steady-state Kalman filtering theory, a new approach of designing Wiener state estimators is presented, whose principle is that based on steady-state Kalman filter and predictor given in the Wiener filter form, and using the autoregressive moving average (ARMA) innovation model, the recursive version of non-recursive steady-state optimal state estimators yields the Wiener state estimators. The proposed Wiener state estimators can handle the state filtering, smoothing and prediction problems in a unified framework. They have the ARMA recursive form, and have asymptotic stability and optimality. A simulation example shows their effectiveness.

关 键 词：KALMAN滤波 Wiener状态估值器 渐近稳定性 预报器 

分 类 号：TN713[电子电信—电路与系统]