Unveiling Aerial Threats: Enhancing UAV Classification Through Radar Digital Twins
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) present significant security concerns due to their widespread utilization in various nefarious activities, including acts of terrorism and military operations involving explosive payloads. Conventional radar-based methods for detection and classification primarily rely on range-Doppler signatures, which may result in misclassification, particularly in distinguishing UAVs equipped with explosives. To mitigate this challenge, this study proposes an algorithm based on Inverse Synthetic Aperture Radar (ISAR) for classification. The proposed algorithm is developed and validated using radar digital twins to generate extensive datasets. Initially, a Machine Learning (ML) algorithm is trained on a dataset containing range-Doppler information to differentiate between a standard commercial quadcopter and the same quadcopter modified to carry explosives. However, the ML model exhibits limited accuracy in classifying instances where the quadcopter is laden with explosives based solely on range-Doppler data. Subsequently, the ML model model is retrained using a dataset incorporating ISAR images for both scenarios. Upon application to a distinct dataset featuring ISAR images of a quadcopter carrying explosives, the model demonstrates enhanced classification accuracy. This study offers valuable insights for the future development of robust countermeasures to mitigate the evolving security challenges posed by UAVs in sensitive environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".