UNIVERSITY OF CALGARY Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications
Bibliographic record
Abstract
This research aims at enhancing the accuracy of land vehicular navigation systems by integrating GPS and Micro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). This comprises improving the MEMS-based inertial output signals as well as investigating the limitations of a conventional Kalman Filtering (KF) solution for MEMS-IMU/GPS integration. These limitations are due to two main reasons. The first is that a KF suppresses the effect of inertial sensor noise using GPS-derived position and velocity as updates but within a limited band of frequency. The second reason is that a KF only works well under certain predefined dynamic models and convenient input data that fit these models, which are not attainable with the utilization of MEMS-based inertial technology. Therefore, if the GPS reference solutions are lost, the accuracy of standalone MEMS-IMU navigation will drastically degrade over time. The Wavelet Multi-Resolution Analysis (WMRA) technique is proposed in this thesis as an efficient pre-filter for MEMS-based inertial sensors outputs. Applying this pre-filtering process successfully improves the sensors ’ signal-to-noise ratios, removes short-
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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) | 0.000 | 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) | 0.014 | 0.005 |
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".