Evaluation of Orbit, Clock and Ionospheric Corrections from Five Currently Available SBAS L1 Services: Methodology and Analysis
Bibliographic record
Abstract
To meet the demands of civil aviation and other precise navigation applications, several satellite-based augmentation systems (SBASs) have been developed around the world, such as the Wide Area Augmentation System (WAAS) for North America, the European Geostationary Navigation Overlay Service (EGNOS) for Europe, the Multi-functional Satellite Augmentation System (MSAS) for Japan, the GPS (Global Positioning System) Aided GEO Augmented Navigation (GAGAN) for India, and the System for Differential Corrections and Monitoring (SDCM) for Russia. The SBASs broadcast messages to correct satellite orbit, clock, and ionosphere errors to augment the GPS positioning performance. In this paper, SBAS orbit, clock and ionospheric corrections are evaluated. Specifically, the orbit, clock and ionospheric corrections derived from SBAS messages are comprehensively evaluated using data collected from the above mentioned systems over 181 consective days. The evaluation indicates that the EGNOS outperforms other systems with signal-in-space range error (SISRE) at 0.645 m and ionospheric correction accuracy at 0.491 m, respectively. Meanwhile, the accuracy of SDCM is comparable to EGNOS with SISRE of 0.650 m and ionospheric correction accuracy of 0.523 m. For WAAS, the SISRE is 0.954 m and the accuracy of ionospheric correction is 0.505 m. The accuracies of the SBAS corrections from the MSAS and GAGAN systems, however, are significantly worse than those of others. The SISREs are 1.931 and 1.325 m and the accuracies of ionospheric corrections are 0.795 and 0.858 m, for MSAS and GAGAN, respectively. At the same time, GPS broadcast orbit, clock and ionospheric corrections are also evaluated. The results show that there are no significant improvements in the SISRE of the broadcast navigation data by applying SBAS corrections. On the other hand, the accuracy of SBAS ionospheric corrections is still much better than GPS broadcast ionospheric corrections, which could still be beneficial for single-frequency users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".