Improving carrier phase reacquisition using advanced receiver architectures
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Bibliographic record
Abstract
This paper employs advanced GNSS receiver architectures to more quickly reacquire the carrier phase data after a loss of lock. Specifically, a piece-wise control method and a phase prediction architecture are proposed. The piece-wise method takes advantage of different parameters in the control system to produce different transition performance within the tracking loop. With this in mind, the approach divides the reacquisition process into separate periods each with different control system parameters in order to achieve a faster transition process. In the phase prediction architecture, carrier phase measurements are predicted for satellites that have lost lock by integrating the estimated Doppler computed from the navigation solution. Predicted phase quality is evaluated in both empirical and theoretical ways. All algorithms are tested using real data collected under mild to moderate operational conditions.
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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 | 0.000 | 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 | 0.000 | 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 it