广义系统ARMA最优递推状态估值器

ARMA OPTIMAL RECURSIVE STATE ESTIMATORS FOR DESCRIPTOR SYSTEMS

应用现代时间序列分析方法 ,基于 ARMA新息模型和白噪声估值器 ,由非递推状态估值器的递推变形 ,提出了广义系统的 ARMA稳态最优递推状态估值器 .它们具有Wiener滤波器形式 ,可处理带奇异状态转移阵和 /或带相关噪声的广义系统 ,可统一处理滤波、平滑和预报问题 ,且可统一处理广义和非广义系统状态估计问题 .仿真例子说明了其有效性 .

Using the modern time series analysis method,based on ARMA innovation model and white noise estimators,and by recursive version of non recursive state estimators,the ARMA steady state optimal recursive state estimators are presented for descriptor systems which have a Wiener filter form.They can handle the descriptor systems with singular state transition matrices and/or with correlated noises,can handle the filtering,smoothing and prediction problems in a unified form,and can handle the state estimation problems of descriptor and non descriptor systems in a unified framework.A simulation example shows their effectiveness.