Measurement and modelling of the dynamic radar cross‐section of an unmanned aerial vehicle
Bibliographic record
Abstract
The optimal radar detection of miniature unmanned aerial vehicles (UAVs) requires that the radar cross‐section (RCS) of the UAVs be known. Although RCS estimates may be obtained from computer simulation and conventional static RCS measurements, the results may not be accurate given that the dynamic effects of the UAV, such as propeller motion, are absent. In this study, an X‐band tracking radar is developed and used to measure the RCS of a mini‐UAV while the UAV is in flight. Statistical methods are then applied to obtain models of the dynamic RCS for each aspect bin and for the UAV as a whole. For the particular quadcopter considered herein, the results indicate that the dynamic RCS is significantly higher than its static RCS. As a result, a target model developed from the dynamic RCS leads to a 15% increase of the 50% probability of detection range compared with a model based on static RCS measurements.
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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 | 0.000 | 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 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".