Impact of Adverse Weather on Unmanned Aircraft Detect-and-Avoid Performance: Modeling, Simulation, and Operational Mitigations

Abstract

Unmanned aircraft systems increasingly operate in airspace where reliable detect-and-avoid capabilities are required to maintain separation from cooperative and non-cooperative traffic. These systems are exposed to a wide spectrum of adverse weather phenomena that can alter both sensor performance and aircraft dynamics. Practical deployment scenarios in lower airspace, beyond-visual-line-of-sight corridors, and mixed-use terminal areas highlight the need to characterize how rain, fog, low clouds, snow, icing, turbulence, and convective activity affect detect-and-avoid decision quality, and how operational mitigations may be structured. This work develops a modeling and simulation framework that couples stochastic representations of adverse weather with parametric models of airborne and ground-based surveillance sensors, track filters, and conflict resolution logics. Weather is represented as a spatially and temporally correlated disturbance acting simultaneously on electromagnetic propagation, measurement quality, and vehicle motion. The detect-and-avoid system is represented at the level of detection probabilities, false alarm characteristics, state estimation error growth, and trajectory prediction uncertainty, all conditioned on weather intensity and structure. Monte Carlo simulations are used to explore conditions under which separation minima are approached or lost, with emphasis on parameter regimes that are plausible for small and medium unmanned aircraft operating in layered traffic. Results are interpreted in terms of performance envelopes and conservative triggers for operational mitigations, such as adaptive minima, route structure adjustments, or temporary restrictions. The study aims to provide a technically transparent basis for relating measurable weather products to detect-and-avoid performance margins without overstating capability.