Evaluation of Data/Pilot Tracking Algorithms for GPS L2C Signals Using Software Receiver
Bibliographic record
Abstract
The current GPS constellation is being modernized to overcome the limitations of legacy GPS signals. L2C is the civilian signal added to L2 band as a part of modernization efforts. The major change in L2C signal structure is the inclusion of the pilot channel along with the data channel. In the context of tracking, the pilot channel carries the same information about frequency, phase and code errors. This information could be used along with that of the data channel for better tracking performance. This paper investigates the performance of Data/Pilot combined carrier-frequency tracking. A detailed analysis of existing methods to combine the frequency discriminators on data and pilot channel is done. Hybrid discriminators that make use of the data and pilot channel’s coherent integration output directly are proposed. Consequently, the performance of different possible combinations of the discriminators to form estimates of frequency error is analysed. They are evaluated under various ܥ/ levels. The advantages of using different discriminator combinations are demonstrated based on ܥ/ .
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".