Multipath mitigation of GNSS carrier phase signals for an on-board unit for mobility pricing
Bibliographic record
Abstract
Inexpensive navigation-grade receivers are insufficiently accurate for the task of building a Global Navigation Satellite System [GNSS]-based parking meter for urban multipath conditions. Survey-grade instruments that demonstrate cm accuracy are inappropriate, and are two orders of magnitude too expensive, for this mass application. We identify three ways in which a digital signal processor added to a stationary, navigation-grade receiver can add considerable accuracy (in the range of 1-2 m, down from 5-10 m) for a parking meter. First, we apply a pseudo-multipath-based filter and a modified receiver autonomous integrity monitoring [RAIM]-derivative filter to the received carrier phase signals, allowing us to infer which signals are most affected by noise processes and to compute receiver position with the remaining signals for greater accuracy. Second, we take advantage of receiver stationarity to dwell on these signals for several minutes, allowing us to acquire a signal characterization metric that is more stable than might be possible with a non-stationary receiver. This is intended for non-repudiation. As a third step we will later experiment with ways to monitor the multipath behaviour of individual signals on approach to a parking event in a way that may allow us to more effectively weigh our initial signal selection criteria. Independent of these three opportunities, we also take advantage of dual GPS/Galileo receivers, a capability that we simulate in this experiment. Testing of the multipath mitigation filters described in this paper on two simulated GPS/Galileo datasets yielded reductions in the standard deviation of the position estimate that ranged from -4% to 61.6% (avg:34.4%) when compared to the control (unfiltered) position calculation.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".