Improved Navigation Application Precise Point Positioning Method in Railways [铁路导航精密单点定位方法改进及性能验证]
Bibliographic record
Abstract
Traditional navigation and positioning applications in railways adopt DGNSS, a differential reference station network has to be established along tracks in order to meet the requirements of positioning accuracy, which requires high construction and subsequent operation and maintenance costs. Precise Point Positioning (PPP) is one of GNSS positioning techniques, which resolves position, velocity based on code and carrier-phase measurements combined with globally distributed GNSS reference station networks. PPP is capable of obtaining centimeter-level accuracy in static mode and decimeter-level one in kinematic mode. In addition, its performance won't degrade with the increase of distance and no additional reference stations are required. This paper introduced PPP fundamentals based on CSRS-PPP software platform coming from Natural Resources Canada (NRC). The authors analyzed PPP's capacity of real-time, availability and safety issues in typical railway navigation application environments. Then a modified integrated positioning method of PPP/INS-based extended Kalman filter was proposed. The results show that integrated solution could solve the availability issue caused by transitory observation unavailability without degrading the accuracy of positioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".