Wavelet Spectral Techniques for GPS Errors Reduction
Bibliographic record
Abstract
GPS measurements can be modeled as a true range plusother errors such as orbital and clock biases, atmosphericresidual, multipath, and observation noise. Modeling isone approach to deal with some of these errors, if theircharacteristics are known (e.g. troposphere andionosphere errors). Another way to deal with these errorsis filtering in the frequency domain, where all these errorshave different frequency spectrum component. Eacherrors is characterized by a specific frequency band, e.g.the receiver noise can be characterized with highfrequency components, multipath errors, which have lowto medium frequency bands, while the ionospheric andtropospheric errors are at a lower frequency band. Wavelet spectral techniques can separate GPS signal intosub-bands where different errors can be separated andmitigated. This paper introduces two new wavelet spectralanalysis techniques to mitigate DGPS errors in thefrequency domain namely, cycle slip and multipath errors.The first approach in this paper, Wavelet de-trending, isintroduced to remove the long wavelength carrier phasemultipath error in the measurement domain. Thepresented wavelet-based trend extraction model is appliedto GPS static baseline solutions. The second approach inthis paper is introduced to detect and remove cycle sliperror which can be seen as a singularity in the GPS data.The propagation of singularities between the waveletslevels of decomposition is different from the propagationof noise. This characteristic is used to identify thesingularities from noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".