Impact of second-order ionospheric delay on GPS precise point positioning
Bibliographic record
Abstract
Traditionally, in GPS precise point positioning (PPP), ionosphere-free linear combinations of dual-frequency carrier-phase and pseudorange measurements are used. Unfortunately, with these linear combinations only the first-order ionospheric delay term is removed and higher order ionospheric delay terms are usually not taken into account. Such residual error components may deteriorate the PPP solution and slow down the convergence time. In this paper the second-order ionospheric delay term is modeled and it is shown that its effect on GPS satellite orbit varies from 2.3 mm to 23.8 mm in the radial direction, 3.6 mm to 18.8 mm in the along-track direction and 2 mm to 16.3 mm in the cross-track direction. In addition, GPS satellite clock corrections showed a difference of up to 0.067 ns. To examine the effect of the second-order ionospheric delay on the PPP solution, new data sets from several IGS stations were processed using a modified version of GPSPace software, which was originally developed by Natural Resources Canada (NRCan). The modified GPSPace accepts the second-order ionospheric corrections as well as the newly developed US National Oceanic and Atmospheric Administration (NOAA) tropospheric signal delay model (NOAATrop). The estimated precise satellite orbit and clock corrections, with second-order ionospheric delay accounted for, were used in all PPP data processing. It is shown that accounting for the second-order ionospheric delay and applying the NOAATrop model improved the PPP solution convergence time by about 15% and improved the accuracy estimation by 3 mm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".