Improved Method to Detect and Repair Cycle Slip for GNSS Medium–Long Baseline in Real-Time Marine Surveys
Bibliographic record
Abstract
Distance-related errors complicate the resolution of real-time ambiguity in medium–long baseline marine surveys. Therefore, detection and recovery of cycle slips in real time is required to ensure high accuracy of global navigation satellite system positioning and navigation in marine surveys. To resolve this, an improved method was presented, where linear combinations of the triple-differenced (TD) between carriers L1 and L2 were formed for a wide lane and free ionosphere. To overcome severe ill-conditioned problems of the normal equation, the Tikhonov regularization method was used. The construction of a regularized matrix by combining a priori information of known coordinates of reference stations, followed by the determination of the corresponding regularized parameter are suggested. A float solution was calculated for the TD ambiguity. The search cycle slip (TD integer ambiguity) was obtained using the least-squares ambiguity decorrelation adjustment (LAMBDA) method. Using our method, cycle slips of several reference station baselines with lengths of a few hundred to one thousand kilometers were detected in real time. The results were consistent with professional software, with a success rate of 100%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".