广义系统降阶极点配置Kalman估值器

Reduced - order pole assignment Kalman 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计理论,提出了广义系统降价极点配置Kalman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。同经典降阶Kalman滤波方法相比,避免了求解Riccati方程。仿真例子说明了所提的理论和算法的有效性。

Using modern time series analysis method, based on the ARMA innovation model and white noise estimation theory, the reduced -order pole assignment descriptor Kalman estimatros are presented. They can handle the prediction, filtering and smoothing problems in a unified framework, and have a global symptotic stability. Compared with non -reduced -order methods, the computational burden is obviously reduced. Compared with the classical reduced - order Kalman filtering methoded, the solution of Ricatti equation is avoided. A simulation example shows its effectiveness.