A Classification Algorithm for Blind UAV Detection in Wideband RF Systems
Bibliographic record
Abstract
We consider the problem of detecting and localizing the time-frequency span of unmanned aerial vehicles (UAVs) which transmit Wi-Fi-type [1] signals in the ISM band. Interference is assumed to be present ubiquitously in the form of Bluetooth and microwave oven signals. We firstly expose that simple edge detection algorithms based on morphological processing, which are conventionally used to detect signals in spectrogram matrices, do not provide a scalable performance beyond a few RF captures because they suffer from limited hyperparameter generalizability. To overcome this limitation, we propose a support vector machine (SVM) classification algorithm, wherein, we run a sliding detection window across the spectrogram and extract the histogram of ordered gradient (HOG) features. The extracted feature vectors are used to classify the windows as positive or negative, depending on whether a signal of interest is present or not. For experimental evaluation, we generate a synthetic RF dataset containing 150 training captures and 50 test captures, each of duration 90ms, bandwidth 56MHz, and containing about 4.5 Wi-Fi signals on average. The proposed SVM algorithm achieves a mean average precision (mAP) of up to 0.4835, which is significantly higher than the state-of-the-art morphological processing algorithm [11] which only achieves an mAP of 0.3676.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".