GPS Cycle Slip Detection and Correction at Measurement Level
Bibliographic record
Abstract
Carrier phase measurements are much more precise than pseudorange measurements and can be used to achieve very accurate positioning solutions. However, carrier phase measurements require resolution of integer ambiguities before precise positioning can be achieved. The GPS receiver can keep track of the integer number of cycles as long as the receiver maintains lock to the satellite signal. However, in reality, the GPS signal could be interrupted momentary by some disturbing factors leading to a discontinuity of an integer number of cycles in the measured carrier phase. This interruption in the counting of cycles in the carrier phase measurements is known as a cycle slip. When a cycle slip occurs, the Doppler counter would restart causing a jump in the instantaneous accumulated phase by an integer number of cycles. Thus, the integer counter is reinitialized meaning that ambiguities are unknown again. In this event, either the ambiguities need to be resolved again or cycle slips need to be corrected to resume the precise positioning/navigation process. These cycle slips can, to some extent, be detected and fixed to avoid delay and computation complexity attributed to integer ambiguity resolution. Researchers have been addressing the problem of cycle slip detection and correction for the last two decades. This paper provides a detailed survey for available techniques to tackle the problem showing their pros and cons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".