In the Realm of Aerial Deception: UAV Classification via ISAR Images and Radar Digital Twins for Enhanced Security
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) pose significant security challenges due to their widespread use in various malicious activities, including terrorist attacks and wartime operations where explosives are attached to them. Conventional radar-based detection and classification methods often rely on range-Doppler signatures, which may lead to misclassification, especially in identifying UAVs carrying explosive payloads. To address this challenge, this letter proposes an inverse synthetic aperture radar (ISAR)-based classification algorithm. To develop and validate the proposed algorithm, a quadcopter is modeled using radar digital twins to generate comprehensive datasets. Initially, a convolutional neural network (CNN) classifier is trained on a dataset comprising range-Doppler information, aiming to distinguish between a commercial quadcopter and the same quadcopter when it is modified to carry explosives. However, the model fails to accurately classify instances where the quadcopter is carrying explosives-based solely on range-Doppler data. Subsequently, the CNN model is retrained using a dataset containing ISAR images for both scenarios. When applied to a separate dataset featuring ISAR images of a quadcopter carrying explosives, the model demonstrates improved accuracy in classification. Real-measurements further validate these findings, confirming the effectiveness of the proposed ISAR-based classification approach in enhancing radar security against UAV-borne threats. This research presents valuable insights for the future development of robust countermeasures to address the evolving challenges posed by UAVs in security-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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".