Characterizing electromagnetic interference signals for unmanned aerial vehicle geophysical surveys
Bibliographic record
Abstract
ABSTRACT The development of a functional unmanned aerial vehicle (UAV) mounted aeromagnetic system requires integrating a magnetometer payload onboard a UAV platform in a manner that preserves the integrity of the total magnetic field measurements. One challenge when developing these systems is accounting for the sources of in-flight magnetic and electromagnetic interference signals that are greater than the resolvability threshold of the magnetometer. Electromagnetic interference generated by the platform has the potential to be mitigated using several techniques such as magnetic shielding, filtering, or compensation and can be attenuated by strategically positioning the magnetometer at a distance from the UAV. The integration procedure and selection of a mitigation strategy can be informed by characterizing the electromagnetic interference generated by the platform. Scalogram analysis is used to characterize the high-frequency electromagnetic signals generated by multirotor UAV electromagnetic motors. A low-sensitivity (7 nT) vector, fluxgate magnetometer is used to measure the electromagnetic interference generated by two unique multirotor UAVs in a controlled laboratory setting. Results demonstrate three spectrally distinct electromagnetic signals, each with unique frequency and amplitude, generated by each UAV platform. The frequency of these electromagnetic interference signals is found to be directly proportional to the applied rotation frequency of the electromagnetic motor. The aforementioned knowledge is applied to UAV field surveys to assess the high-frequency electromagnetic interference signals experienced. This is achieved using a high sensitivity (0.01 nT), scalar optically pumped magnetometer with a 1000 Hz sampling frequency. Our results indicate that adequate sensor placement and preflight evaluation of the platform-sensor interactions provide useful mitigation strategies, which can compensate for electromagnetic interference signals generated by the UAV platform during aeromagnetic surveys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".