Bibliographic record
Abstract
Global Navigation Satellite Systems (GNSS) have proven to be a viable and reliable solution in interference-free environments and in presence of Line-of-Sight (LOS) signals only. However, in urban canyons, multipath signals directly affect the pseudorange measurements resulting in degraded positioning performance of traditional GNSS receivers. Moreover, traditional GNSS receivers cannot distinguish between non-LOS (NLOS) and LOS signals, resulting in even worse performance if the receiver tracks NLOS-only signal. Hence, NLOS and multipath signals remains a dominant source of error in satellite-based navigation. Most of the existing research has focused on identifying and rejecting NLOS measurements. However, little research has used NLOS signals constructively. In this regard, this research uses snapshots of GNSS data in order to estimate position, utilizing all NLOS signals constructively with the help of a 3D Building Model (3DBM). Using a 3DBM and a ray-tracing algorithm, the number of reception paths and the corresponding path delays of reflected signals is predicted across a grid of candidate positions. These predictions are then used to compute least-squares fit to the GNSS receiver’s correlator outputs and the position with smallest residuals is selected as the position estimate. This approach is termed Signal Delay Matching (SDM) and yields a solution that is nearly unaffected by traditional GNSS error sources, and has capability of providing a position solution using a single satellite only. The use of snapshots of data mean the receiver need not perform tracking operations, thus making it easier to implement and power efficient. The feasibility and performance of the algorithm was tested using data collected in downtown Calgary, Canada, where buildings reach heights of over 200 m. Contrary to traditional approaches, results for the proposed method show that positioning error decreases as sky-visibility decreases. For sky-visibility below 20%, the median error was found to be just over 3 m. Compared to two pseudorange-based receivers, the proposed method yields RMS errors improvements of 22% to 48% in the horizontal plane.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".