Calibration of 3-Axis Low-Cost Magnetometer Using the Least Square Ellipsoid Fitting Algorithm
Bibliographic record
Abstract
This paper presents a calibration method for low-cost 3-axis magnetometers using the least square ellipsoid fitting algorithm. The aim of the calibration process is to reduce noise and mitigate the effects of magnetic interferences and instrumentation errors, thereby enhancing the accuracy and reliability of magnetometer measurements. By collecting data while moving the sensor in arbitrary directions, the calibration parameters are estimated, including magnetic disturbances (soft iron and hard iron effects) and instrumental errors (scale factor, nonorthogonality, and bias). The measured data are modeled as a combination of these errors, and the calibration parameters are obtained by solving a quadratic form equation using the least square ellipsoid fitting algorithm. The results demonstrate that the proposed calibration method using the least square ellipsoid fitting algorithm provides a valuable contribution to the field of magnetometer calibration, with the calibrated data exhibiting a better fit to the surface of an ellipsoid compared to the original magnetometer data, indicating its effectiveness, achieving 90% accuracy in magnetometer calibration of module MPU-9250. The proposed calibration method offers several advantages, including its simplicity and cost-effectiveness. Furthermore, the real-time capability of the algorithm makes it suitable for applications that require continuous calibration, ensuring accurate and reliable measurements over time. The integration of the calibration method into the intelligent IMU Sensor (IIS) further enhances its practicality and applicability in real-world scenarios.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".