MODIFIED EXTENDED KALMAN FILTERING AND A REAL-TIME PARALLEL ALGORITHM FOR SYSTEM PARAMETER-IDENTIFICATION

被引：19

作者：

CHUI, CK

CHEN, GR

CHUI, HC

机构：

[1] TEXAS A&M UNIV SYST,DEPT ELECT ENGN,COLLEGE STN,TX 77843

[2] RICE UNIV,DEPT ELECT & COMP ENGN,HOUSTON,TX 77251

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1990年 / 35卷 / 01期

关键词：

D O I：

10.1109/9.45155

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The most popular real-time filtering algorithm for nonlinear systems is perhaps the extended Kaiman filter which will be called EKF for brevity. In this note, a modification of the EKF algorithm, which will be called MEKF for short, is introduced. The modification is achieved by an improved linearization procedure. For this purpose, a parallel computational scheme is recommended, and it has immediate applications to identifying unknown system parameters of time-varying linear stochastic state-space models in real-time. It should be noted that just as the EKF, our MEKF is also ad hoc in the sense that it is a real-time approximation method. Numerical examples with computer simulation are included in this note to demonstrate the effectiveness of this new procedure over the EKF algorithm. © 1990 IEEE

引用

页码：100 / 104

页数：5