Machine Learning for UAV Classification Employing Mechanical Control Information
Why this work is in the frame
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Bibliographic record
Abstract
Range-Doppler images are widely used to classify different types of Unmanned Air Vehicles (UAVs) because each UAV has a unique range-Doppler signature. However, a UAV's range-Doppler signature depends on its movement mechanism. This is why a classifier's accuracy would be degraded if the effect of the mechanical control system of UAVs wasn't taken into consideration, which may lead to a non-unique signature of a UAV while in-flight. In this paper, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of two quadcopters, a hexacopter, and a helicopter UAVs on their range-Doppler signatures. A Mechanical Control-Based Machine Learning (MCML) algorithm is introduced to classify the four UAVs. Different Machine Learning (ML) algorithms were applied to the generated datasets that considered the mechanical control information of UAVs. The Convolutional Neural Networks (CNN) algorithms provided robust performance reaching an accuracy of higher than 90%.
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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 | 0.000 |
| 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 it