Kinematic precise point positioning heights enhancement using static measurements and Voronoi’s corrector surface
Bibliographic record
Abstract
Recently, the use of Precise Point Positioning (PPP) has spread widely.Unlike Relative Positioning techniques, PPP uses only a single receiver unit.Although it provides sub-centimeter horizontal accuracy, the vertical accuracy of PPP is a hot topic in the research community.In this research, an approach to enhance the accuracy of PPP estimated heights is proposed via the integration of static measurements and corrector surfaces produced based on the Voronoi diagram.The performance of PPP Static and Kinematic measurements was evaluated by comparing their solutions with solutions obtained from Relative Positioning techniques.The Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) was used to process measurements collected at the study area of 39 km2 along the coastal zone of the Mediterranean Sea in the northern Delta region of Egypt.Based on the estimated results, the proposed approach significantly reduces the RMSE of the height differences.The average improvement ratio is approximately 73.9%, with the RMSE decreasing from 10.9 cm to 2.79 cm.Moreover, about 95.7% of the 279 tested point height differences show values within ±5 cm or better after applying this approach.Notably, PPP using the proposed approach saved approximately 50% of the time required for the Relative Positioning technique.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".