Advancing Precise Point Positioning With All-Source CSSR Message Augmentation
Bibliographic record
Abstract
Real-time estimation of a precise ephemeris poses a formidable challenge for precise point positioning (PPP); such estimation potentially leads to degraded positioning accuracy and a long convergence time. In particular, the satellite-specific errors resulting from satellite positions, clock biases and code biases, can cause positioning errors on the order of meters, with the convergence time in the range of hours. To address this problem, next-generation services are provided by the Quasi-Zenith Satellite System, Galileo system and BeiDou system. These services broadcast satellite augmentation data through modernized signals in the compact state space representation (CSSR) format for use in realtime PPP. However, the availability of a single service is limited because it is not adaptable to different environments or regions. In this study, we proposed an advancing PPP by using all sources of satellite-based correction data. This method allows the number of usable satellites to reach 25 in a dynamic scenario because it maximizes the number of valid CSSR corrections. The proposed advanced CSSR-PPP addresses the problem of limited service availability, thus expanding applicability of real-time PPP with satellite-based augmentation data. Furthermore, a positioning engine was developed and validated through kinematic experiments. This engine achieved a horizontal root mean square error of 0.19 m and a 95th-percentile horizontal error of 0.28 m by using a low-cost global navigation satellite system receiver in a suburban region. The proposed CSSR-PPP method is competitive with the postprocessing-based Canadian Spatial Reference System PPP service.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".