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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".