@article{DAI2025, 
author = {Yeying DAI and Rui SUN and Siyu DENG and Li JI and Yuanyuan WANG and Xuedong HUANG},
title = {Grid error modeling aided GNSS/IMU integrated navigation comprehensive quality control algorithm},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {9},
pages = {3174-3182},
keywords = {GNSS/IMU integrated navigation, pseudorange error modelling, quality control, weighted least square, fault detection},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0495},
doi = {10.13700/j.bh.1001-5965.2023.0495},
abstract = {In the complex urban environment, Global navigation satellite system (GNSS) signals are prone to non-line-of-sight reception (NLOS) and multipath interference (MI) due to the occlusion and reflection of obstacles such as tall buildings. The positioning accuracy and reliability are seriously reduced, which cannot meet the user’s high precision and reliability positioning, navigation and timing (PNT) service requirements. Signal classification and multipath modeling methods based on machine learning and data-driven are of great significance for alleviating GNSS multipath effect and improving positioning accuracy in urban areas. But the accuracy, efficiency and adaptivity of such models still need to be improved. In this paper, a grid error modeling aided GNSS/Inertial measurement unit (IMU) integrated navigation comprehensive quality control algorithm is proposed. Besides the grid pseudorange error modeling, it proposes a refined comprehensive quality control strategy based on grid fitting accuracy and satellite fault detection, thus optimizes the performance of GNSS/IMU integrated navigation in complex urban environments. The field test in urban environment shows that compared with the traditional GNSS/IMU integrated navigation algorithm, the horizontal and 3D positioning accuracy of the proposed algorithm are improved by 50.23% and 66.77%, respectively, and compared with the grid pseudorange error modeling algorithm, they are improved by 11.56% and 40.53%, respectively.}
}