Time-Frequency Analysis using V-band Radar for Drone Detection and Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Detection and classification of unmanned aerial vehicles (UAV), namely drones, is an active area of research. Radars are all-weather instrument that operate in various frequency bands and are used for monitoring drone activities. However, little experimental work has been done with radars that operate in the V-band (40 GHz to 75 GHz) portion of the electromagnetic spectrum. This paper presents drone detection data collected using a 66 GHz research radar from aiRadar Inc. Micro-Doppler signatures of the rotating drone rotors are extracted using short-time Fourier transform (STFT) and continuous wavelet transforms (CWT). It was determined that the complex Morlet wavelet provides more detailed micro-Doppler features that can be used for classifying different 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 | 0.001 |
| 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