Use of magnetic quasi static field (QSF) updates for pedestrian navigation
Bibliographic record
Abstract
This paper assesses a novel method of using a quasi-static magnetic field to provide updates to the navigation (i.e. attitude) filter. The method is able to make use of magnetometer measurements in a perturbed magnetic field, under the condition that the field's magnitude remains constant for short periods of time. The fact that magnetometer measurements can still be used in perturbed environments makes this update significant in terms of incorporating the magnetometer measurements into a navigation solution. The QSF process requires a detection algorithm to first identify quasi-static fields and second to perform the update. Thus this paper applies the QSF algorithm in a navigation filter to assess its performance in GNSS degraded or denied environments. Data sets are used to assess QSF updates. These range from open athletic fields to deep indoors where GPS signals are denied. The environments vary in terms of soft iron effects. The data was collected with high grade miniature MEMS IMUs, a high sensitivity GPS receiver and a low cost magnetometer. An accurate reference solution is derived from a tactical grade IMU. For the backpack mounted IMU the application of QSF updates yielded a 56 % heading error improvement when used as a heading reference system. For a corresponding ankle mounted system the application of QSF updates yielded a 56 % improvement in the position error (RMS) when used as a pedestrian navigation system. The maximum error over a 45 minute GPS outage decreased from 208 m to 128 m. The updates do not significantly decrease the estimated gyro error state variances, indicating that it is more suited for gyros and magnetometers with a lower performance than those used herein.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".