广义系统降阶Wiener状态估值器

REDUCED-ORDER WIENER STATE ESTIMATORS FOR DESCRIPTOR SYSTEM

应用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,利用奇异值分解,提出了广义系统降阶Wiener状态估值器。它可统一处理滤波,平滑和预报问题,且具有渐近稳定性。在计算上与非降阶的方法相比明显地减少了计算负担。同多项式方法相比避免了求解Diophantine方程。仿真例子说明了其有效性。

Using modern time series analysis method and singular value decomposition,based on the ARMA innovation model and white noise estimation theory,the reducedorder Wiener state estimators are presented for Descriptor ststem.They can handle prediction,filtering,and smoothing problems in a unified frame work and have a symptotic stability.Compared with nonreducedorder methods,the computational burden is obviously reduced.Compared with the polynomial approach Diophantine equation is avoid.A simulation example shows its effectiveness.