UAV Classification using Neural Networks and CAD-generated Radar Datasets
Bibliographic record
Abstract
Using micro-doppler signatures is an effective way to classify different types of UAVs, as well as other airborne objects such as birds. To generate signatures for drones, radar measurements are needed; however, these measurements are limited to the types of available drones, the radar parameters, the targets’ range, and the environments in which these measurements are conducted. In this paper, a new method for generating signature datasets is introduced. The method uses full-wave electromagnetic simulation software. Using this method, radar drones’ datasets can be generated using different types, sizes, drone materials, radar parameters, detected range, targets speed, and rotor RPM for rotary drones. A 77 GHz modeled FMCW radar is used to create dataset for classification purposes. Finally, a Convolutional Neural Network (CNN) algorithm is used to classify five types of drones. Based on the results, the classification of the drones is found to exceed 97% accuracy.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".