An efficient and robust maneuvering mode to calibrate low cost magnetometer for improved heading estimation for pedestrian navigation
Bibliographic record
Abstract
Personal navigation systems intend to provide the navigation information in any environment, indoors and outdoors, and at any time. In outdoor environments, the positioning solution is typically provided by using Global Positioning System (GPS). However, GPS is inaccurate or unavailable in most of indoor environments and therefore other externally-referenced sensing techniques are required. Inertial sensing techniques are used for pedestrian navigation in association with dead reckoning approach. Magnetometers can be used to derive the user’s heading by sensing the Earth’s magnetic field. In this paper, an efficient and robust maneuvering mode to calibrate low cost magnetometer is recommended for pedestrian navigation applications. Additionally, other maneuvering modes and errors associated with each mode to achieve best estimation for the calibration parameters in the 3D Space are also provided. Also, the effect of using different maneuvering modes (DMM) on the heading estimation for the pedestrian navigation is studied. The results show that the coordinated mode is suitable to perform the calibration process as the unit is rotated in a way to cover the whole 3D space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".