@inproceedings{2deaa15e55d648c494045e77b48234c4,
title = "UKF based fault detection and state estimation for nonlinear systems with correlated noise",
abstract = "State estimation and fault diagnosis are essential topics for dynamic systems. Unscented Kalman filter(UKF) has been widely applied in nonlinear systems. The classical UKF algorithm is built on the premise that process noise and measurement noise is independent. In practical problems, this assumption is not always satisfied. In addition, due to the limitation of communication and sensor fault, etc., data missing or unreliable measurements will happen inevitably. Therefore, it is very important to study the state estimation of nonlinear systems with unreliable measurements and correlated noise. In this paper, an UKF based state estimation algorithm with unreliable observations under correlated noise is presented. A numerical example is given to show the feasibility and effectiveness of the presented algorithm.",
keywords = "correlated noise, fault detection, nonlinear system, state estimation",
author = "Hongxue Chen and Liping Yan and Yuanqing Xia and Mengyin Fu and Bo Xiao and Guangzheng Hu and Huifang Li",
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.2017.8028193",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5294--5299",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "United States",
}