@inproceedings{698149244df646aab9c3029ac29e5255,
title = "An adaptive robust UKF initial alignment algorithm",
abstract = "In the case that the initial alignment angle of inertial navigation system is large and does not satisfy the hypothesis of small alignment angle, a nonlinear error model is needed to describe the attitude error of inertial navigation system, and a nonlinear algorithm is used to estimate the alignment angle. The unscented Kalman Filter (UKF) is selected as the filtering algorithm for the combined system. Due to the problem that the current UKF algorithm has poor adaptive ability, and the current adaptive UKF algorithm is easy to be affected by unknown noise characteristics of the system, an improved introduction method of adaptive fading factor is proposed. Simulation results show that the proposed method has higher accuracy in estimating the misalignment angle when the prior information is inaccurate.",
keywords = "adaptive UKF, inertial navigation, initial alignment, large misalignment angle",
author = "Huaijian Li and Tao Wang and Xiaojing Du and Tianhang Yan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/DOCS55193.2022.9967731",
language = "English",
series = "2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022",
address = "United States",
}