Measuring GNSS Multipath Distributions in Urban Canyon Environments
Bibliographic record
Abstract
In general, standalone global navigation satellite systems (GNSS) receiver architectures cannot provide a position accuracy suitable for use in vehicular applications in urban canyon scenarios. Specifically, GNSS signals are affected by the surrounding objects, such as high buildings, trees, and so on, which introduces multipath errors. Multipath arises from the reception of reflected or diffracted signals, possibly in addition to the line-of-sight signal, and is one of the most detrimental error sources in GNSS positioning applications. Multipath distributions in the urban canyon area are measured and characterized in this paper. In particular, the Doppler and code phase delay under different conditions are assessed as a function of vehicle speed and signal power, which are different from previous calibration metrics. Specifically, multipath directional-dependence phenomenon (i.e., the variation resulting from the direction of travel of the user) is observed during this process, and the multipath maximum Doppler offset and minimum Doppler offset are derived and verified by the real data. The multipath distribution will eventually affect the search strategy (i.e., search space size, coherent integration time) utilized in the high sensitivity receiver.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".