Detection of continuous ground-based acoustic sources via unmanned aerial vehicles
Bibliographic record
Abstract
This paper presents the results of experiments that were conducted to partially establish the viability of utilizing acoustic sensing as a sense-and-avoid system for unmanned aerial vehicles (UAV). Experiments were conducted in which a UAV fitted with four acoustic sensors was flown under autopilot control at various altitudes over a loud speaker located on the ground acting as a continuous acoustic source. Various physical and digital noise reduction techniques were employed to enhance the recorded signals to allow increased detection ranges. Using standard free-field acoustic attenuation laws, maximum expected detection distances were calculated and presented. Depending on the acoustic source frequency signature and filtering method used, maximum expected detection distances ranged anywhere from 0.45 to 4.21 km on average with signal-to-noise ratios ranging from 10.3 to 37.0. Based on the results obtained, it is expected that acoustic techniques could facilitate the detection of an airborne source (aircraft) at distances great enough to facilitate an avoidance maneuver.
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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".