Radar-Based Drone Detection Using Complex-Valued Convolutional Neural Network
Bibliographic record
Abstract
With an unprecedented growth in the number of commercially available drones, the detection of drones is becoming increasingly essential. Deep learning-based convolutional neural network (CNN) models utilizing micro-Doppler signatures, are being widely used for drone detection applications. Radar returns from a drone and its corresponding micro-Doppler signatures are often complex-valued. However, the CNNs only consider the magnitude component of the micro-Doppler signatures while ignoring the phase component. This phase component contains essential information that can supplement the magnitude for enhanced drone detection. Thus, this paper proposes a novel complex-valued CNN that considers the magnitude and phase component of the radar returns. This paper also investigates the performance of the proposed model with radar returns of different sampling frequency and duration. A comparative analysis of the performance of the proposed model in the presence of noise is also presented. The proposed complex-valued CNN model achieved the highest detection accuracy of 93.80% when the radar returns were sampled at 16000 Hz and for duration of 0.01s. This shows that the proposed model can successfully detect drones that appear in the radar for an extremely short interval of time.
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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 | 0.000 |
| 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".