On the Impact of an Antenna Field of View on the Classification of UAVs
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Bibliographic record
Abstract
Detection and classification of Unmanned Air Vehicles (UAV s) at a distance have become important because of the potential threats of the illegal usage of them. Radar systems are preferred for UAV s detection because of their advantages over other UAVs detection systems. In this paper, an investigation of the effect of an antenna Field of View (FoV) on Machine Learning (ML) accuracy is conducted. A full-wave Electromagnetic (EM) CAD tool is used to generate the required datasets for this investigation. Five UAV s were used in this work, a fixed-wing, a helicopter, two quadcopters, and a hexacopter UAVs. The ML algorithm was trained on a relative angle of 0° between the UAV s and the antenna, and it was tested on relative angles of 20°, 40°, 60°, 80°, and 90° between the UA V s and the antenna. The ML classification accuracy decreases with the increase of the relative angle between the UAV s and the antenna. The accuracy of a classifier can be estimated by employing Multiple-input Multiple-output (MIMO) radars to detect the Angle of Arrival (AoA) of drones and the relative angle between the drones and the antenna.
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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