A new magnetic compass calibration algorithm using neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The magnetic compass can provide heading direction by measuring the Earth’s magnetic field. In practical applications, there usually exists an unwanted local magnetic field that will distort the magnetometer measurements; hence a calibration procedure is essential. Current calibration methods are limited by the inaccurate magnetometer error estimation when measurements are deteriorated by magnetic disturbances or large noises. This paper proposes a new compass calibration algorithm via modelling the nonlinear relationship between the compass heading and the true heading using neural networks. When an external heading reference is available, neural networks can be trained to properly model this nonlinear input–output pattern even in the presence of magnetic disturbances, and subsequently can be applied to convert the compass heading into the correct heading. The proposed algorithm does not require declination information and magnetometer biases and scale factor estimation. The simulation and field test results have verified the effectiveness and robustness of the proposed calibration method and have also shown that the calibration performance is proportional to the quality of the training data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it