Analysis of Multiple GPS Antennas for Multipath Mitigation in Vehicular Navigation
Bibliographic record
Abstract
The most important issue when using GPS for urban vehicular navigation is the position reliability which usually depends on the nature of the environment. In particular, the presence of urban canyons and foliage can cause significant degradation in satellite visibility as well as high multipath. Although GPS information tends to be integrated with other dead-reckoning sensors to increase position availability, it is often difficult to isolate satellites with significant multipath effects, which can then corrupt the integrated position solution. The objective of this paper is to assess the feasibility of using multiple antennas to isolate and detect multipath on pseudoranges so they can be rejected before they contaminate estimated vehicle positions. One of the properties of multipath is that it decorrelates rapidly as a function of distance, so antennas spaced at least 0.5 m apart may be subjected to different multipath conditions such that detection may be possible. Land tests were conducted with four antenna/receivers in Calgary under various environments including open sky, urban canyon and dense foliage conditions. The correlation of multipath between the antennas is analyzed for each of these environments, as is the overall satellite availability and multipath as a function of the environment. A multipath mitigation technique based on statistical reliability testing is presented.
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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 | 0.003 |
| 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) | 0.000 | 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 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".