| Application of Kalman filtering in VLBI data analysis T Nilsson, B Soja, M Karbon, R Heinkelmann, H Schuh Earth, Planets and Space 67, 1-9, 2015 | 69 | 2015 |
| JTRF2014, the JPL Kalman filter and smoother realization of the International Terrestrial Reference System C Abbondanza, TM Chin, RS Gross, MB Heflin, JW Parker, BS Soja, ... Journal of Geophysical Research: Solid Earth 122 (10), 8474-8510, 2017 | 60 | 2017 |
| Estimation and evaluation of real-time precipitable water vapor from GLONASS and GPS C Lu, X Li, M Ge, R Heinkelmann, T Nilsson, B Soja, G Dick, H Schuh GPS solutions 20, 703-713, 2016 | 53 | 2016 |
| Ensemble machine learning of Random Forest, AdaBoost and XGBoost for vertical total electron content forecasting R Natras, B Soja, M Schmidt Remote Sensing 14 (15), 3547, 2022 | 51 | 2022 |
| The IVS data input to ITRF2014 A Nothnagel, W Alef, J Amagai, PH Andersen, J Anderson, T Andreeva, ... GFZ Data Services, 2015 | 42 | 2015 |
| Earth orientation parameters estimated from VLBI during the CONT11 campaign T Nilsson, R Heinkelmann, M Karbon, V Raposo-Pulido, B Soja, H Schuh Journal of Geodesy 88 (5), 491-502, 2014 | 40 | 2014 |
| Tropospheric delay determination by Kalman filtering VLBI data B Soja, T Nilsson, M Karbon, F Zus, G Dick, Z Deng, J Wickert, ... Earth, Planets and Space 67, 1-16, 2015 | 35 | 2015 |
| Optimal VLBI baseline geometry for UT1-UTC Intensive observations M Schartner, L Kern, A Nothnagel, J Böhm, B Soja Journal of Geodesy 95 (7), 75, 2021 | 28 | 2021 |
| Neural ODE differential learning and its application in polar motion prediction M Kiani Shahvandi, M Schartner, B Soja Journal of Geophysical Research: Solid Earth 127 (11), e2022JB024775, 2022 | 18 | 2022 |
| Inclusion of data uncertainty in machine learning and its application in geodetic data science, with case studies for the prediction of Earth orientation parameters and GNSS … MK Shahvandi, B Soja Advances in Space Research 70 (3), 563-575, 2022 | 18 | 2022 |
| Probing the solar corona with very long baseline interferometry B Soja, R Heinkelmann, H Schuh Nature Communications 5 (1), 4166, 2014 | 18 | 2014 |
| Testing general relativity with geodetic VLBI-What a single, specially designed experiment can teach us O Titov, A Girdiuk, SB Lambert, J Lovell, J McCallum, S Shabala, ... Astronomy & Astrophysics 618, A8, 2018 | 16 | 2018 |
| Multi-technique comparison of atmospheric parameters at the DORIS co-location sites during CONT14 R Heinkelmann, P Willis, Z Deng, G Dick, T Nilsson, B Soja, F Zus, ... Advances in Space Research 58 (12), 2758-2773, 2016 | 15 | 2016 |
| Ultra-short-term prediction of LOD using LSTM neural networks J Gou, M Kiani Shahvandi, R Hohensinn, B Soja Journal of Geodesy 97 (5), 52, 2023 | 14 | 2023 |
| Determination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data B Soja, T Nilsson, K Balidakis, S Glaser, R Heinkelmann, H Schuh Journal of Geodesy 90, 1311-1327, 2016 | 14* | 2016 |
| Discontinuity detection in GNSS station coordinate time series using machine learning L Crocetti, M Schartner, B Soja Remote Sensing 13 (19), 3906, 2021 | 13 | 2021 |
| Data driven approaches for the prediction of Earth's effective angular momentum functions MK Shahvandi, J Gou, M Schartner, B Soja IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | 12 | 2022 |
| Earth orientation parameters from VLBI determined with a Kalman filter M Karbon, B Soja, T Nilsson, Z Deng, R Heinkelmann, H Schuh Geodesy and Geodynamics 8 (6), 396-407, 2017 | 12 | 2017 |
| Small geodetic datasets and deep networks: attention-based residual LSTM autoencoder stacking for geodetic time series M Kiani Shahvandi, B Soja International Conference on Machine Learning, Optimization, and Data Science …, 2021 | 11 | 2021 |
| Modeling of residual GNSS station motions through meteorological data in a machine learning approach P Ruttner, R Hohensinn, S D’Aronco, JD Wegner, B Soja Remote Sensing 14 (1), 17, 2021 | 10 | 2021 |
Tobias NilssonLantmäterietEmail verificata su lm.se
