Effects of urban canyons and electromagnetic interference on RPAS performance
Bibliographic record
Abstract
Remotely Piloted Aircraft Systems (RPAS), or drones, can carry out a variety of missions in urban environments, including traffic monitoring, civil infrastructure inspection and photography. However, drone pilots can face multiple command and control challenges during urban operations. Dense urban areas, where streets are flanked by high buildings on both sides, can create urban canyons. In this environment, signal propagation can be blocked or received through multiple paths by reflection, refraction or scattering off building surfaces, which diminishes signal quality. Drones rely on the Global Navigation Satellite System (GNSS) for navigation requiring an unobstructed line of sight to the satellites. Buildings can reduce the quantity of satellites visible to the drone and impair the drone’s flight control system. Probability models of signal line of sight are presented, along with simulations to understand the influence of certain parameters such as flight altitude, width between buildings, and the Fresnel zone on the probability of reception. The influence of other physical factors such as weather conditions and antenna positioning is also discussed. Electromagnetic interference (EMI) can affect a drone’s navigation system and command and control (C2) link. Both natural sources and artificial sources can cause EMI. The main EMI sources in urban areas are presented and types of soft failure are classified according to their level of impact. The distinction between front-door EMI and back-door EMI (based on the route through which it enters the circuits of RPAS electronics) is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".