Intelligent Suppression of Non-Maneuvering Magnetic Interference of Aeromagnetic UAV
Bibliographic record
Abstract
In the aeromagnetic survey based on a fixed-wing unmanned aerial vehicle (UAV), the non-maneuvering magnetic interference generated by the magnetic components can significantly reduce the data quality of the airborne magnetometer. Aircraft layout modification is a standard method of addressing this problem. However, the existing layout modification methods rely heavily on personal experience and cannot precisely determine the overall layout of multiple magnetic components quantitatively and cooperatively. Even if the layout of magnetic components is determined through multiple experiments, obtaining an optimal suppression effect of the magnetic interference is difficult. An intelligent suppression method of non-maneuvering magnetic interference is proposed to address this problem. An eccentric multi-magnetic dipole (EMMD) model that can accurately characterize the primary magnetic components is established; then, an intelligent cooperative optimization method for the layout of magnetic components based on the Aquila Optimizer (AO) algorithm is proposed to quantitatively determine the optimal combination of positions and orientations of multiple components. A dedicated experimental platform, including a non-magnetic rotating stage, a three-axis fluxgate magnetic sensor, an optically pumped magnetometer, and an aeromagnetic tester, was built to validate the proposed characterization model and layout optimization method. The experimental results observed in a non-magnetic laboratory demonstrated that the goodness-of-fits of the EMMD model to the total-field and three-component magnetic interference generated by the magnetic components are all above 0.932. Furthermore, the total-field intensities of magnetic interference in the magnetometer areas located at the left and right wingtips were suppressed by 98.7% and 98.9%; the magnetic inhomogeneities in the two areas were reduced by 97.7% and 98.4%; the magnetic imbalance between the two wingtips was reduced by 95.5%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".