@inproceedings{7088ccf1edb4464d81626c186fce5d37,
title = "GNSS position estimation based on unscented Kalman filter",
abstract = "Extended Kalman Filter (EKF) is widely applied to Global Navigation Satellite System (GNSS) position estimation. But EKF lacks stability and degrades performance for nonlinear problems because it just linearizes nonlinear systems. To overcome the shortcomings of the EKF, the unscented Kalman filter (UKF) has been proposed. Unscented Kalman filter (UKF) is an improved Kalman filter for nonlinear systems. The UKF does not require the linearization of the system models. Alternatively it uses a set of deterministically selected {"}sigma-points{"}, which completely capture the true mean and covariance of the original random vector. Then these sigma-points are propagated through the nonlinear models. The algorithm is based on a non-linear Unscented Transformation (UT transform) to recur and update the covariance of the nonlinear model's state and error. The result of the simulation shows that the accuracy and performance of the algorithm are better than EKF and Kalman Filter(KF).",
keywords = "Covariance, Nonlinear systems, Position estimation, UKF",
author = "Fule Zhu and Yanmei Zhang and Xuan Su and Huan Li and Haichao Guo",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Conference on Optoelectronics and Microelectronics, ICOM 2015 ; Conference date: 16-07-2015 Through 18-07-2015",
year = "2016",
month = feb,
day = "3",
doi = "10.1109/ICoOM.2015.7398793",
language = "English",
series = "2015 International Conference on Optoelectronics and Microelectronics, ICOM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "152--155",
booktitle = "2015 International Conference on Optoelectronics and Microelectronics, ICOM 2015",
address = "United States",
}