摘要
采用卡尔曼滤波方法,针对带有观测时滞的线性离散系统,研究了输入白噪声最优估计器的设计.通过对观测序列进行重新组织,使之成为无时滞的观测,并进一步给出重组新息序列.由Hilbert空间上的正交投影定理,通过求解与原系统同维的两个Riccati方程实现递推计算.该方法能避免状态扩维,有效地减轻了计算负担.最后通过仿真实例说明该方法的有效性.
The optimal input white noise estimator for linear discrete-time systems with delayed measurements is studied by using Kalman filtering. The delayed measurements are re-organized as that without delay, then the re-organized innovatipn is given. Based on the projection theorem in Hilbert Spaces, the proposed approach is given in terms of two Riccati difference equations （RDEs） with the same order as that of the original system. The approach can improve the computational efficiency without resorting to system state augmentation. A numerical example is given and the simulation results show the effectiveness of the proposed method.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2009年第6期63-68,共6页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(60774004
60804034)
山东省自然科学基金资助项目(Z2007G01
Y2007G34)
关键词
白噪声估值器
新息重组
RICCATI方程
时滞系统
去卷
white noise estimators
re-organized innovation
R＇iccati equation
time-delay systems
deconvolution
