Range performance evaluation from the flight tests of a passive electro-optical aircraft detection sensor for unmanned aircraft systems
Bibliographic record
Abstract
The range performance evaluation of a multi-camera electro-optical aircraft detection instrument, “DragonflEYE,” was conducted. A “range at first detection” (R0) quantity, evaluated from the temporal signal-to-noise ratio of potential targets on a collision course, is proposed as a generic metric for evaluating electro-optical systems. The methodology and evaluation process are discussed. Efficacy of the approach was tested by flying multiple collision trajectories, with the instrument mounted onto a Bell 205 helicopter acting as a surrogate unmanned aircraft system, while an instrumented Bell 206 Jet-Ranger acted as the intruder. The R0 values were extracted and subsequently compared to visual estimates by the flight crew. A mean detection range of R0 = 6.3 ± 1.7 km was observed to be within the margin of error for flight-crew detection range of 4.8 ± 2.0 km. Sensitivity analysis was conducted on the choice of threshold and the sensor’s angular resolution, with increased resolution, yielded diminishing returns due to atmospheric extinction. Robustness was assessed by repeating the experiment on a different day with a secondary camera array, “Cerberus,” recording images simultaneously. The observed detection ranges were within the margin of error of prior estimates. In addition, measured ranges from Cerberus aligned with their predicted values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".