UNIVERSITY OF CALGARY Development of a Multi-Sensor GNSS Based Vehicle Navigation System By
Bibliographic record
Abstract
iv A vehicle navigation system is developed that uses a combination of GPS positioning and dead reckoning sensors. A survey of dead reckoning sensors is presented, including examples and comparisons of different technologies. The dead reckoning sensors chosen for the final implementation were a low cost piezoelectric vibrating gyro and differential odometry provided by a vehicle’s anti-lock braking system. Filtering of the data is done by a centralized Kalman filter with independent calibration filters for the dead reckoning sensors. Augmentations of GPS positioning are also investigated, focusing on clock coasting using an OCXO and height aiding. Several series of tests were performed to analyze the system’s performance, culminating with tests in downtown Calgary under urban canyon masking and multipath conditions. Results from the testing indicate that an accuracy on the order of 10 to 20 metres can be achieved under most circumstances, but more intelligent algorithms or map matching are needed to control the large deviations which can be caused by poor GPS solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".