An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates
Bibliographic record
Abstract
ABSTRACT: In the last decade, the Land-Vehicle Navigation (LVN) market has grown rapidly. For most LVN systems, GPS is used for positioning. However, GPS has poor accuracy in urban areas due to signal blockages. Therefore, the LVN market has targeted the integration of other sensors with GPS. In this case, sensors' cost and size are major issues. Recent advances in MEMS inertial sensors made it possible to develop low-cost and compact IMUs. However, MEMS provide poor accuracy when used without updates (e.g., during GPS outages). In such periods, other updates are required for better performance. Vehicle full-stops, i.e., Zero-Velocity-Updates (ZUPTs), are usually applied for this purpose. However, this is not practical especially when GPS blockages are frequent. In this paper, 3D Auxiliary Velocity Updates (AVUs) are used, namely, non-holonomic constraints and odometer-derived velocity. Using kinematic MEMS/GPS data with several GPS signal blockages, the results showed a significant accuracy improvement after applying AVUs.
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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 | 0.000 |
| 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) | 0.001 | 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".