Improved kinematic Precise Point Positioning performance with the use of map constraints
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A positioning approach combining satellite measurements with a map representing the ground-truth trajectory is developed with the main objective of improving the availability of solutions for a mobile vehicle. For the positioning model, the Precise Point Positioning (PPP) technique is augmented with an alternative map-matching to find a probable space where the true vehicle or platform position is located. Then, by using a selection criterion based on the precise carrier phase residuals, the best candidate position within the space can be determined. This process provides an accurate initial position to the PPP filter, different from the standard PPP approach that relies on a point position using the less accurate pseudorange observables. A controlled experiment of a mobile receiver navigating over a pre-defined trajectory was conducted. The results show that the approach offers an instantaneous initial convergence, eliminating the re-convergences during two GNSS obstructions of 32 and 17 seconds, while constantly keeping the solution on the correct trajectory, even when tracking 3 to 2 satellites. This approach outperforms the standard PPP and RTK solutions in terms of convergences and re-convergences. These results are corroborated when comparing the average and standard deviation of residuals to the standard PPP model. For the pseudorange residuals, improvements of 17.5 cm and 24.3 cm in the average and standard deviation respectively were achieved. The carrier phase residuals standard deviation of the proposed approach was 3 cm better than that of the standard PPP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it