ii THE UNIVERSITY OF CALGARY Error Analysis and Stochastic Modeling of MEMS based Inertial Sensors for Land Vehicle Navigation Applications
Bibliographic record
Abstract
iii Although GPS measurements are the essential information for currently developed land vehicle navigation systems (LVNS), the situation when GPS signals are unavailable or unreliable due to signal blockages must be compensated to provide continuous navigation solutions. In order to overcome the unavailability or unreliability problem in satellite based navigation systems and also to be cost effective, Micro Electro Mechanical Systems (MEMS) based inertial sensor technology has pushed the development of low-cost integrated navigation systems for land vehicle navigation and guidance applications. In spite of low inherent cost, small size, low power consumption, and solid reliability of MEMS based inertial sensors, the errors in the observations from the MEMS-based sensors must be appropriately treated in order to turn the observations into useful data for vehicle position determination. The error analysis would be conducted in the time domain specifying the stochastic variation as well as error sources of systematic nature. This thesis will address the above issues and present algorithms to identify and model the
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".