Coherent, Noncoherent, and Differentially Coherent Combining Techniques for Acquisition of New Composite GNSS Signals
Bibliographic record
Abstract
The growing demand of location, navigation and positioning services is boosting the development of new signals and modulations that will be adopted by new global navigation satellite systems (GNSS), such as the European Galileo, the Chinese Compass and the modernized GPS. A common feature of these new modulations is the presence of two channels, the data and pilot components, that separately carry the navigation message and the ranging information. Three different techniques, noncoherent combining, coherent combining with sign recovery and differentially coherent combining, are analyzed for the joint acquisition of data and pilot signals. For each acquisition strategy the probabilities of detection and false alarm are provided. In particular closed-form expressions for the probabilities of coherent channel combining and of the differentially coherent integration strategy are derived. To the best of our knowledge these expressions are new. Monte Carlo simulations are used to support theoretical analysis demonstrating the accuracy of the proposed models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".