A Novel GNSS Positioning Technique for Improved Accuracy in Urban Canyon Scenarios Using 3D City Model
Why this work is in the frame
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Bibliographic record
Abstract
eliable positioning in urban canyon, especially in dense urban areas, is difficult to achieve in a cost-effective manner using standalone Global Navigation Satellite System (GNSS), due to multipath problems and Non-Line-of-Sight (NLOS) signals. In this regard, several researches have focused towards identifying and rejecting NLOS measurements. However, very few researches have used NLOS signals, although generating final position using only one of the signals. In this regard, this research utilizes all the available signals, including all NLOS signals, from all the satellites, in order to estimate position, using an algorithm based on concept of constructive use of NLOS signals. The NLOS signals are used constructively by incorporating the information related to nearby reflectors, with the help of a 3D city model. The feasibility and performance of the algorithm was done using a real data collected in Downtown Calgary. In a way, this research attempts towards building a low cost GNSS based technology for improved navigation performance, in scenarios where fewer satellites are available and rejecting measurements due to blunders might cost a crucial stake of availability.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 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 it