Benefit of partial L2C availability to estimate ionospheric delay for dual-frequency GPS ambiguity resolution
Bibliographic record
Abstract
This paper evaluates the benefit of the partial availability of GPS satellites with L2C signal capability in estimating the ionospheric delay using the dual-frequency L1/L2 code and phase measurements using real data. Compared to the strategy of estimating one slant ionospheric delay (SID) for each satellite, a simplified single differential zenith ionospheric delay (ZID) method is proposed to account for the ionospheric effect using the limited number of L2C measurements. The algorithms and models are implemented in a Kalman filter (KF) based code and phase observations processor using between receiver single difference (SD) GPS L1/L2 observations. Using data sets from three International GNSS Service (IGS) stations with both L2C and L2P code measurements, the performance of using L1 observations only, L1/L2 dual-frequency observations without estimating ionospheric delay, and L1/L2 dual-frequency observations with estimating either SID or differential ZID is compared in terms of ambiguity resolution (AR) and positioning accuracy in conjunction with ionospheric delay estimation. The results show L1/L2 AR outperforms L1 only in several scenarios, and the proposed method improves vertical accuracy of the fixed position approximately 10 cm with estimating the single differential ZID.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".