Radar-Based Digital Twins for Classification of UAVs and Avian Targets
Why this work is in the frame
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Bibliographic record
Abstract
In this study, the efficacy of range-Doppler imaging is explored for the detection and classification of Unmanned Air Vehicles (UAVs), with attention to the radar system’s operating frequency and bandwidth. The investigation employs full-wave Electromagnetic (EM) CAD software to scrutinize the influence of varied radars, spanning different frequency bands, on the precision of range-Doppler images of a rotating blade. Notably, mmWave radars, distinguished by their expansive bandwidth, demonstrate superior range-Doppler accuracy compared to other examined radar systems. Building on this, a subsequent inquiry is undertaken to evaluate the performance of Machine Learning (ML) algorithms in drone classification amid the presence of avian organisms. The mmWave radar is modeled using EM CAD tools to generate diverse datasets encompassing a quadcopter UAV and avian subjects. Employing two distinct ML algorithms, the study reveals that an increased avian presence diminishes the radar’s ability to effectively detect and classify drones. The CNN model achieves 99% classification accuracy when a single bird coexists with the drone, declining to 90% in scenarios featuring a drone amidst a swarm of ten birds. We believe that our presented workflow presents a paradigm shift in how defense scientists can validate possible counter measures against illicit uses of compact drones.
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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