3D building model-assisted multipath signal parameter estimation
Bibliographic record
Abstract
Multipath remains a dominant source of error in satellite-based navigation, despite a great deal of effort by researchers and receiver manufacturers to reduce it. Mitigation of multipath errors is especially difficult when the Doppler shift between the direct and secondary propagation paths is very small (approximately 1-2 Hz) and this is usually the case in urban canyons where the reflectors are almost parallel to the direction of motion of the receiver. Multipath parameter estimation is one option; however, the method needs the number of secondary paths in order to estimate the multipath parameters. Moreover, in weak signal environments, the problem becomes more challenging. In this paper we present multipath parameter estimation assisted with a 3D building model to provide the number of secondary paths as well as initial estimates for the relevant delays. The estimation is based on a Least Squares estimation technique using a grid of correlators. The concept is proven using simulated data and tested using real data. Result shows that the accuracy of estimated parameters improves significantly if the estimator is aided with 3D building model information.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".