Radar-Based Digital Twins for Classification of UAVs and Avian Targets
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".