Studying the Effects of Clutter Using V-Band Radar for Drone Classification
Bibliographic record
Abstract
Radars are all-weather instruments that operate in various frequency bands and therefore are useful for detecting and monitoring unmanned aerial vehicles (UAV) such as drones. Majority of the research in drone classification are carried out in controlled, clutter-free indoor environments. This work presents drone detection data collected using a 66 GHz research radar from aiRadar Inc in both indoor and outdoor environments. Time-frequency analysis techniques, namely short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are used to extract micro-Doppler signatures of the rotating propellers. A qualitative comparison was done for the radar returns of a quadcopter and an octocopter, both for indoors and outdoors environment. It was discovered that the spectrograms and scalograms are noisier outdoors compared to indoors. It was also determined that the scalograms from CWT produced more distinctive features compared to STFT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".