摘要
应用时域上的现代时间序列分析方法，基于ＡＲＭＡ 新息模型和白噪声估计理论，提出了广义系统的Ｗｉｅｎｅｒ 状态估值器和稳态Ｋａｌｍａｎ 估值器．它们可统一处理最优滤波、平滑和预报问题．它们避免了求解Ｄｉｏｐｈａｎｔｉｎｅ方程和Ｒｉｃｃａｔｉ 方程，因而构成了Ｗｉｅｎｅｒ 滤波和Ｋａｌｍａｎ 滤波新方法．
Using the modern time series analysis method in the time domain,based on the autoregressive moving average(ARMA)innovation model and white noise estimation theory,Wiener state estimators and steady state Kalman estimators are presented for descriptor systems. They can handle the optimal filtering,smoothing and prediction problems in unified frameworks. They avoid the solution of the Diophantine equations and Riccati equations, and constitute new approaches to Wiener filtering and Kalman filtering. Two simulation examples show the usefulness of the new approaches.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
1999年第5期634-638,共5页
Control Theory & Applications
基金
国家自然科学基金!(69774019)
关键词
广义系统
WIENER滤波
KALMAN滤波
descriptor systems
descriptor Wiener state estimators
descriptor Kalman filter
modern time series analysis method
