Time Correlation in GNSS Positioning over Short Baselines
Bibliographic record
Abstract
Ignoring the temporal correlations present within global navigation satellite systems (GNSS) observations can result in too much confidence being placed in the estimated positions. Temporally correlated errors occur when the magnitude of an error is similar over time. Treating temporally correlated GNSS errors as independent results in an overly optimistic variance-covariance (VCV) matrix, potentially resulting in an incorrect fix of the ambiguities and an overly optimistic estimated accuracy of the estimated positions. Unlike spatially correlated errors, temporally correlated errors are not mitigated or properly dealt with within most of today’s real-time kinematic (RTK) software. This paper reviews the theory of temporal correlations as well as previously developed solutions for obtaining more realistic position accuracies. A simulation is developed demonstrating the impact of neglecting temporal correlation. Using real data from five baselines up to 10 km in length, this paper then determines how long the L1 carrier phase observations remain correlated. By using the autocorrelation of the phase residuals, the L1 phase measurements are shown to be correlated for an average of 115 s. The length of temporal correlation varies according to receiver environment and satellite elevation angle. Trends in correlation time according to baseline length are also studied.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".