Optimal and self-tuning state estimation for singular stochastic systems: A polynomial equation approach

被引：0

作者：

Zhang, HS ^{[1
]}

Xie, LH ^{[1
]}

Soh, YC ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

PROCEEDINGS OF THE 39TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5 | 2000年

关键词：

singular stochastic systems; optimal state estimation; innovation analysis; polynomial equation approach; self-tuning estimation;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is concerned with the optimal steady-state estimation for singular stochastic discrete-time systems using a polynomial equation approach. The key to the optimal estimation is to calculate an optimal estimator gain matrix. The main contribution of the paper is to present a simple method for computing the gain matrix. Our method involves solving one simple polynomial equation which is derived based on the uniqueness of the ARMA innovation model. The approach covers the prediction, filtering and smoothing problems. Further, when the noise statistics of model are not available, self-tuning estimation is performed by identifying one ARMA innovation model.

引用

页码：3807 / 3812

页数：6

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