THE UNIVERSITY OF CALGARY Improving Tracking Performance of PLL in High Dynamic Applications
Bibliographic record
Abstract
The Phase-locked loop (PLL) is used in GPS receivers to track an incoming signal and to provide accurate carrier phase measurements. However, the PLL tracking performance and measurement accuracy are affected by a number of factors, such as signal-to-noise power ratio, Doppler frequency shift, the GPS receiver’s jitter caused by vibration, and the Allan deviation. Among these factors, the thermal noise and Doppler shift are the most predominant and have a large influence on the design of the PLL. In high dynamic situations, the conflict between improving PLL tracking performance and the ability to track the signal necessitates some compromises in PLL design. This thesis investigates the strategies to resolve this conflict. Three methods are investigated to improve PLL tracking performance in high dynamic applications: a Kalman filter-based tracking algorithm, application of a wavelet de-noising technique in PLL, and an adaptive bandwidth algorithm. The Kalman filter-based tracking algorithm makes use of a carrier phase dynamic model and a measurement from the output of the discriminator to estimate the phase difference between the incoming
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".