Combined Radar and Camera Drone Detection in Urban Environment: A Simulation-Based Approach
Bibliographic record
Abstract
Unmanned Airborne Systems (UAS) have gained popularity in recent years. Drone pilots sometimes operate in restricted areas where they can involuntarily disrupt human activities, they sometimes deliberately conduct illicit activities, or some can weaponize their UAS. A significant challenge associated with counter-UAS is the disproportionate cost difference between the detection/mitigation systems and customer-grade UASs. In this paper, we focus on the cost-efficient detection of UAS activities in urban environments. More specifically, we present a simulation platform designed to study the concurrent use of AI-powered camera systems and radar. Those AI-powered camera systems can be sold as software stacks that are supposed to be camera-agnostic. The objective of our simulation approach is to ease the selection of camera models, lenses, and the positioning of the cameras in order to complement radar coverage.
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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".