TransXLT: A novel ZTD prediction method with SASR-based data reconstruction

Shicheng Xie; Xuexiang Yu; Jiajia Yuan; Xu Yang; Mingfei Zhu; Yuchen Han; Min Wei; Zhongchen Guo

doi:10.1016/j.isci.2025.112328

TransXLT: A novel ZTD prediction method with SASR-based data reconstruction

iScience. 2025 Mar 31;28(5):112328. doi: 10.1016/j.isci.2025.112328. eCollection 2025 May 16.

Authors

Shicheng Xie^{1

2}, Xuexiang Yu^{1

2}, Jiajia Yuan^{2

3}, Xu Yang^{2

3}, Mingfei Zhu^{1

2}, Yuchen Han^{1

2}, Min Wei⁴, Zhongchen Guo⁵

Affiliations

¹ School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China.
² Anhui Provincial Joint Laboratory of Urban 3D Real-Scene and Intelligent Security Monitoring, Huainan 232001, China.
³ School of Geomatics, Anhui University of Science & Technology, Huainan 232001, China.
⁴ School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
⁵ School of Environment and Surveying Engineering Suzhou University, Suzhou 234099, China.

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.

Keywords: Applied sciences; Computer science; Navigation System.