Lightweight Convolutional Neural Network-based Drone Detection Using Radar Spectrograms
Bibliographic record
Abstract
In this paper, we present a preliminary investigation of a lightweight convolutional neural network (CNN) architecture as a drone detection method using the long window Short-time Fourier transform (STFT) spectrograms of radar signals reflected from various drones as input. The micro-Doppler signatures within these signals manifest as horizontal Helicopter Rotation Modulation (HERM) lines in the spectrograms, providing a distinguishing feature for identifying the presence or absence of drones. The radar data used in this paper was collected in an outdoor setting with a commercially available V-band radar. The proposed network is trained using the collected limited dataset. We compare the detection performance of the proposed lightweight CNN-based detector with that of the pre-trained MobileNetV2 model after applying transfer learning with the collected dataset. Despite training with the limited dataset, the lightweight CNN model provides similar detection performance with only 35% trainable parameters compared to MobileNetV2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".