Assessment of wavelets analysis for carrier-phase multipath mitigationThis article is one of a series of papers published in this Special Issue on the theme <i>GEODESY</i>.
Bibliographic record
Abstract
In carrier-phase measurements, which are the most precise observations for Global Positioning System (GPS) relative positioning, multipath error is still a factor that interferes with achieving the desired accuracy. Various improvements in receiver and antenna technologies, as well as modeling strategies, have resulted in better ways of coping with this error source. However, errors caused by multipath can be as large as 5 cm, which is not an acceptable accuracy, especially in precise surveying applications like deformation monitoring. In this paper, a full assessment of different wavelets techniques that can be used in multipath mitigation is made to evaluate the optimum way of using wavelets to reduce or remove this type of error. Also, a new approach based on the wavelet detrending technique is introduced to remove carrier-phase multipath error in the measurement domain. To mitigate multipath, GPS double-difference observables are fed to an adaptive wavelet analysis procedure based on high- and low-pass filter decomposition with different levels of resolution. Consequently, the observable sequences are corrected; these corrected observables can then be used to reduce the ambiguity search volume during the initial float solution stage. Meanwhile, double-difference observations with multipath mitigation offer an efficient method for obtaining a better baseline solution.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".