Detecting drones with radars and convolutional networks based on micro-Doppler signatures
Bibliographic record
Abstract
The detection of drones using radars is a problem of great importance due to the wide proliferation of drones that are being used in a variety of applications. In this paper, we propose a novel approach to convolutional neural network (CNN)-based drone detection using radar micro-Doppler signatures. The CNNs are trained on micro-Doppler signatures obtained from short-time Fourier transform spectrograms of the time-series data of the radar reflections from the drones. In particular, we investigate the binary classification of drones versus noise using both simulated data and real data taken from rapidly-manoeuvring drones. First, we train a CNN to detect and classify drones using simulated data based on the Martin-Mulgrew (MM) model. We find that at a 10-decibel signal-to-noise ratio, this CNN performs with an F1 score greater than 0.8. Furthermore, we apply transfer learning on the trained model to adapt it to real data. We show that this use of transfer learning improves the results over a standalone model trained solely on real data by 0.075 F1 score points.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".