Efficient Dual-Frequency Ambiguity Resolution Algorithm for GPS-Based Attitude Determination
Bibliographic record
Abstract
This paper discusses the benefits of using dual-frequency receivers for attitude determination applications. The ambiguity resolution performance, baseline, and attitude solution accuracy is compared between single and dual-frequency modes. Evaluation is performed both in static and kinematic environments. While the static data were collected in an open-sky environment, the two kinematic data sets were collected in “degraded” environments including partial blockages due to trees or overpasses. The level of dynamics observed during these tests is somewhat limited, as the vehicle often travels at constant speed, but is, however, representative of car navigation. Results indicate that for baselines as long as 10m, the proposed WL/L1 cascaded ambiguity resolution scheme significantly outperforms its single frequency counterpart by providing faster and more reliable fixed ambiguity resolution. Assuming a correct ambiguity fix, dual frequency observations do not provide significant improvements in terms of baseline or attitude accuracy. However, the improved speed and reliability of the dual frequency ambiguity resolution algorithm does translate, especially in kinematic mode, into significant improvements in terms of attitude solution availability and reliability.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".