WAAS Performance Improvements as a Result of WAAS Expansion
Bibliographic record
Abstract
The objective of the Wide Area Augmentation System (WAAS) is to enhance the GPS standard positioning service providing sufficient integrity, accuracy, availability and continuity for use in commercial aviation. The system provides en-route through non-precision approach, Lateral NAVigation / Vertical NAVigation (LNAV/VNAV), and Localizer Performance with Vertical guidance (LPV) approach capabilities. WAAS is the first Satellite Based Augmentation System (SBAS) certified for use in commercial aviation. The FAA commissioned the WAAS system for operational use on July 10, 2003. WAAS has been in continuous operation since commissioning. The first user equipment was certified to use WAAS for LPV approaches in October 2003. The WAAS Full LPV performance contract is currently underway. The contract expands WAAS with new reference stations in Alaska, Canada and Mexico, an additional master station, additional GEO satellites and algorithm changes to improve integrity, accuracy and availability with focus on ionospheric algorithms. The first set of new reference stations and algorithms have been installed in the field and data is being collected to validate WAAS performance. The system will be upgraded in a series of planned release updates. This objective of this paper is to document the performance improvements of the WAAS system for the existing releases and future planned releases. Although there are improvements to integrity and accuracy, these are less visible to the aviation user. A few general comments will be made relative to integrity and accuracy improvements. The primary metric of interest is the LPV availability. The existing and projected availability is shown for the initial system, current system and the projected system at several points along the way to full LPV performance. The availability performance is displayed graphically showing the percent availability attained throughout the service volume. Summary tables show the percent of various regions (CONUS, Alaska, Mexico, and North America) that achieve 95%, and 99% availability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".