A Combined Multiple Reassignment Squeezing and Ergodic Hough Transform Method for Hovering Rotorcraft Detection from Radar Micro-Doppler Signals
Bibliographic record
Abstract
The rapid increase in production of small unmanned rotorcrafts (SURs) has made real-time drone surveillance critical for airspace security. Effective SUR detection is essential for maintaining aviation safety, protecting privacy, and ensuring public security. However, conventional radar systems struggle to detect hovering SURs due to their low velocity and small radar cross-section (RCS), which make them nearly indistinguishable from stationary clutter. To address this issue, this paper proposes a hovering SUR detection method through identifying the micro-Doppler signal (MDS). By applying the multiple reassignment squeeze processing and exhaustive Hough transform, the proposed approach effectively enhances the accumulation of micro-Doppler signal generated by the rotor blades, which enables the separation of hovering SUR signals from stationary clutter. Numerical simulations and field experiments validate the effectiveness of the proposed method, demonstrating its potential for micro-Doppler signal detection using a UHF-band horizontally co-polarized radar system.
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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 | 0.000 |
| 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 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".