@inproceedings{c7d4eed0a24a4cf2976b2b5d3f77ab14,
title = "EKF-based state estimation for nonlinear complex networks",
abstract = "This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks. A recursive state estimator is developed by employing the structure of the extended Kalman filter (EKF) with coupling terms. By using the stochastic analysis technique, an upper bound is derived for the coupling strength to guarantee the boundedness of the estimation errors in the mean square sense. A numerical example involving localization of mobile robots is provided to illustrate the effectiveness of the proposed estimator.",
keywords = "Complex networks, Extended Kalman filter, State estimation",
author = "Jian Sun and Wenling Li and Yingmin Jia and Junping Du",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8027597",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "1702--1707",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "美国",
}