Effect of Constraining a Limited Number of Slant Ionospheric Corrections in PPP and an Improved Partial Tight-Constraining Algorithm
Bibliographic record
Abstract
With external ionospheric corrections, precise point positioning solutions can be augmented with proper constraints, enabling fast convergence and high-precision positioning performance. However, there are two main issues in user algorithms. Firstly, the contribution of the number of applied ionospheric corrections on positioning performance is not clear to users. For instance, in low-cost sensors where computation resources are limited, applying too many corrections (e.g., tens of corrections) from server will possibly cause slow response. Secondly, the determination of constraining strength for corrections from different satellites remains challenging. Therefore, this paper evaluates the effect of the number of ionospheric corrections on positioning errors and convergence time, and proposes an improved tight-constraining algorithm for partial reliable corrections. Static and simulated kinematic positioning results confirm that the more corrections applied, the better the positioning performance. However, the positioning accuracy only has limited improvements when several corrections are already applied, especially in the north direction. The proposed method reduces static positioning root-mean-square errors by 43.4%, 26.9%, and 32.1% in the east, north, and up directions, respectively, compared to solutions without constraint. In simulated kinematic results, the convergence time is further shortened by 7.7%, 4.8%, and 2.5% for the east, north, and up directions, respectively, compared to cases where all corrections are applied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".