Year: 2025
Type: article
Abstract: Traditional Zenith Tropospheric Delay (ZTD) models often face difficulties in maintaining prediction accuracy under complex meteorological conditions and data loss. To address this, we propose the transformer-xLSTM (TransXLT) model, which integrates spatial-temporal information from global navigation satellite system (GNSS) stations, ERA5 (global atmospheric reanalysis), and GPT3 (empirical ZTD estimation). Missing data are reconstructed using a sparse attention-based time series reconstruction (SASR) method. Experimental results show: (1) under a 120-h data loss, SASR reduces mean absolute error (MAE) by 24.5% compared to cubic Hermite interpolation; (2) SASR lowers training root mean square error (RMSE) by 15.1% versus direct data deletion; and (3) TransXLT achieves an average RMSE of 8.13 mm across six sites, reducing RMSE by up to 76.54% compared to benchmarks like CNN-LSTM and ERA5. Demonstrating robustness across varying latitudes, altitudes, and seasons, the model significantly advances ZTD estimation accuracy for GNSS applications.
Source: iScience
Authors: Shicheng Xie, Xuexiang Yu, Jiajia Yuan, Xu Yang, Mingfei Zhu, Yuchen Han, Min Wei, Zhongchen Guo
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,240
Funders: Dalian High-Level Talent Innovation Program, National Natural Science Foundation of China, Anhui University, Anhui University of Science and Technology, State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Anhui Provincial Key Research and Development Plan, Major Science and Technology Projects in Anhui Province
OpenAlex ID: W4409051960