Hybrid Cycle Slip Detection Method for Smartphone Global Navigation Satellite System
Bibliographic record
Abstract
The main roadblock to precise Smartphone GNSS positioning is erroneous carrier phase data which is highly prone to cycle slips (CSs). There is a dearth of research on cycle slip detection and repair (CSDR) methods for Smartphone GNSS. Existing literature on CSDR methods is based on phase data captured by professional-grade receivers. These methods can be broadly categorized into two groups: (1) Geometry-Free CSDR (GF-CSDR) and (2) Geometry-Based CSDR (GB-CSDR). GF-CSDR methods rely on individual satellite measurements, whereas the GB-CSDR technique considers the whole satellite–receiver geometry. This paper proposes a real-time single-frequency CSDR method combining both GF and GB-CSDR. Experimental results are presented for a static and a kinematic smartphone dataset. It is shown that although reliable CS detection is possible for Smartphone GNSS, CS repair reliability is limited due to the carrier phase precision quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".