Multiresolutional characterization and mitigation of GNSS signal for robust positioning
Bibliographic record
Abstract
While the use of wavelet filtering on applications such as audio and video is known, in this research. wavelet filters are applied as a practical tool to improve positioning accuracy of a navigatioll-grade receiver in challenging environments. A single, stationary recel\,'er operating OIl the L1 frequency, and collecting data in 15-minute segments, was used to obtain pseudoranges which were then used to compute positions. The magnitudes of these pseudoranges are often overstated due to multipath. ~Iultipath mitigation was applied tothese signals using a hvo-stage wavelet filter. The first stage operates in the pseudorange domain to remove bias error and the second stage operates in the position domain to minimize the effect of the low velocities that existed among the stationary positions. This filtering had a marked effect of reducing positioning scatter (variance). To measure the effect of this filtering, several statistical moments (before and after filtering), were compared. Throughout datasets studied, the unfiltered position scatters tend to be markedly non-Gaussian showing extreme effects of skew and kurtosis in addition to high variance. The position scatters after filtering tend to be highly Gaussian with far lower degrees of skew and kurtosis. In this study, the results obtained from the data sets sho'Ned significant improvement, less than 1.5 m with a probability of 96.5%, in standard deviation of the estimated positions.,
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".