Event based estimation with correlated noises

被引：4

作者：

Chitraganti, Shaikshavali ^{[1
]}

Abkerane, Samir ^{[2
]}

机构：

[1] Indian Inst Technol Palakkad, Dept Elect Engn, Palakkad 678557, Kerala, India

[2] Univ Lorraine, CRAN CNRS UMR 7039, F-54500 Vandoeuvre Les Nancy, France

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2019年 / 356卷 / 13期

关键词：

TRIGGERED STATE ESTIMATION; NONLINEAR-SYSTEMS; NETWORKED SYSTEMS; GAUSSIAN FILTER; KALMAN FILTER; DESIGN; FUSION;

D O I：

10.1016/j.jfranklin.2019.06.018

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For a linear system having event based measurements and correlated noises, a state estimation algorithm is proposed. A general event based sampling is employed to obtain the measurements, where in the case of unavailability of measurements, event based strategy itself is used to obtain approximate state and covariance estimates. To deal with correlated noises, a two-step ahead prediction approach is employed to obtain recursive equations for estimated state and covariance. The obtained results are illustrated using a simulation example. (C) 2019 Published by Elsevier Ltd on behalf of The Franklin Institute.

引用

页码：7533 / 7547

页数：15

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