Analysis of L5/E5 acquisition, tracking and data demodulation thresholds
Bibliographic record
Abstract
The combined use of proposed GALILEO signals and current and also future GPS signals will help users to greatly improve positioning accuracy, continuity of service, availability and integrity. The performances brought by all these signals are degraded by the same perturbations such as multipath, effects of ionosphere or jamming. However, differences in signal designs and environments imply differences in protection against these perturbations. One type of critical criterion is the set of the key operations thresholds in receivers such as acquisition, tracking and data demodulation. The aim of the submitted paper is to propose required signal power to noise spectral density ratio to perform acquisition, tracking and data demodulation given desired performances. Some of the considered performances are acquisition false alarm, detection probabilities, mean acquisition time and bit error rate. These thresholds are computed for GPS L5 and Galileo E5a/E5b signals. The paper starts with a brief review of studied signal relevant characteristics such as central frequency, code frequency and period, data rate and used coding and minimum received C/N0. After this, the second part is dedicated to the presentation of the different methods that can be used for acquisition, tracking and data demodulation thresholds computation. These thresholds take into account some degradations originating from the front end bandwidth, the tracking errors and acquisition times. Intrasystem and intersystem interference are considered through the use of the interference coefficient. Benefits from pilot channels and data coding are also highlighted. Obtained results enable to estimate C/N0 margins with respect to minimal available C/N0 for different GNSS signals
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.006 | 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".