@article{SHENG2026, 
author = {Qi SHENG and Rui SUN and Yulin HE and Hengyu ZHANG},
title = {A robust adaptive positioning algorithm for GNSS/IMU based on 3D grid error modeling},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {5},
pages = {1701-1711},
keywords = {pseudorange error modeling, robust filtering, adaptive filtering, GNSS/IMU integrated navigation, non-line-of-sight},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2024.0169},
doi = {10.13700/j.bh.1001-5965.2024.0169},
abstract = {In complex urban environments, tall buildings cause Global Navigation Satellite System (GNSS) signals to suffer from Non-Line-of-Sight (NLOS) propagation and Multipath Interference (MI), which degrade the positioning accuracy of intelligent transportation systems. The existing two-dimensional grid-based multipath modeling method has shortcomings including insufficient precision in the height direction and an overly simplistic adjustment strategy for measurement noise covariance. This article proposes a GNSS/inertial measurement unit (IMU) robust adaptive filter algorithm based on 3D grid error modeling. By dividing the height space on the basis of the existing 2D grid, fine modeling can be further achieved. In the stage of multipath error prediction, the grid-center-matching method is used to alleviate the model prediction error caused by incorrect matching. Then, we propose a filtering model selection strategy based on the multipath error predictions. Moreover, according to the robust theory, we propose a robust threshold dynamic adjustment strategy to update the measurement noise covariance adaptively. The positioning performance of GNSS/IMU integrated navigation in complex urban environments can be significantly improved by the proposed algorithm. The 3D positioning accuracy of the suggested algorithm has improved by 27.57% when compared to the 2D grid assisted GNSS/IMU robust adaptive algorithm and by 48.44% and 31.51%, respectively, when compared to the traditional GNSS/IMU tight combination algorithm and the traditional GNSS/IMU robust adaptive algorithm, according to the results of urban environment vehicle experiments.}
}