Cycle Slip repair for single-frequency smartphone GNSS using the Best Integer Equivariant estimator
Bibliographic record
Abstract
This paper proposes an instantaneous cycle slip repair method for single-frequency smartphone GNSS carrier phase measurements. In it, cycle slips are detected using a hybrid method that combines geometry-based and geometry-free cycle slip detection paradigms. A float value of the cycle slip is estimated after a Kalman filter (KF) update, and a cycle-slip repair is attempted as a form of integer ambiguity resolution problem using five different classes of estimators: (i) Integer Least Squares (ILS), (ii) Best Integer Equivariant (BIE) estimator (iii) Integer Aperture with Fixed Failure-rate Ratio Test (IA-FFRT) (iv) Partial Ambiguity Resolution (PAR) and (v) PAR with FF-RT. The PAR with FF-RT estimator uses a model-driven ratio test to select the subset of ambiguities chosen to be fixed. The performance of all five estimators is compared and analyzed using real smartphone GNSS carrier phase measurements. The variance of the fixed/repaired cycle slips is estimated. BIE and PAR-FFRT are the top-performing estimators, with PAR-FFRT offering the greatest precision improvement after repairing the float cycle slip ambiguities. BIE, on the other hand, has a 100% repair rate without the need for validation testing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".