Real-Time Mitigation of Measurement Noise Arising From Geomagnetic Background Interferences for a Coil Vector Magnetometer
Bibliographic record
Abstract
For the existing coil vector magnetometers, the negative effect of the geomagnetic background interference generated by solar activity has hardly been considered, making it difficult to further improve the solution precision for the magnetic direction. To address this issue, we propose a novel method of mitigating the measurement noise arising from the geomagnetic background interference. We combine the total field with the synchronous component data of the three-axis fluxgate magnetic sensor to correct the resultant field after applying a bias field in real time, and then propose a new measurement algorithm of magnetic inclination and declination to reduce noise. In theoretical evaluation, we model the measurement noise and then verify the effectiveness of the proposed method. Moreover, we build a dedicated experimental platform to verify the practicality of the proposed method. Comparative observation results obtained in a nonmagnetic room of a geomagnetic observatory show that the proposed method significantly mitigates the measurement noise of the magnetic inclination and declination by 83% and 75%, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".