UNIVERSITY OF CALGARY Design of Galileo L1F Receiver Tracking Loops
Bibliographic record
Abstract
The ever-increasing public demand for location and positioning services has generated a demand for higher performance Global Navigation Satellite Systems (GNSS). The Galileo L1F signal, part of the European contribution to future GNSS, was designed to respond to the above demand in terms of measurement accuracy, tracking robustness and tracking sensitivity. Since the Galileo L1F public signal structure is almost fully specified, this thesis proposes novel tracking loop designs to increase code and phase measurements performance for potential implementation in future receivers. Using detailed modeling of each error that affects phase tracking, the Galileo L1F signal is shown to result in increased phase tracking robustness under dynamics, providing at least a 5-dB higher sensitivity and delivering more accurate measurements than the GPS C/A signal thanks to the availability of a dataless channel. Further phase measurement accuracy can be achieved through two proposed techniques that combine pilot channel phase tracking with the less robust data channel tracking. These techniques still maintain the same high resistance level towards dynamics as pilot channel-only phase tracking,
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".