Time Correlation in GNSS Positioning over Short Baselines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it