On the Impact of an Antenna Field of View on the Classification of UAVs
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".