SKYGUARD: REAL-TIME AIRSPACE RISK SCORING BY FUSING ADS-B AIRCRAFT DATA WITH RF-BASED DRONE DETECTION

Manned aircraft and unmanned aerial vehicles (UAVs) now share the same airspace, yet existing systems either track only manned aircraft (ATC radar and ADS-B receivers) or detect only drones (counter-UAS RF scanners), and no affordable system correlates both to estimate collision risk in real time. This paper presents SkyGuard, a software-based airspace risk-scoring system that fuses Automatic Dependent Surveillance–Broadcast (ADS-B) aircraft data with radio-frequency (RF) based UAV detection data into a unified airspace model. A weighted algorithm computes a dynamic risk index from 0 to 100 using proximity, closest-point-of-approach convergence and aircraft altitude-band severity, scaled by detection confidence; a classification layer (CNN with Remote ID cross-referencing) is designed to distinguish authorized drones from unauthorized or hostile ones; and a four-tier alert scheme (Advisory, Caution, Warning, Emergency) notifies operators through a live web dashboard with a map-based risk overlay. The work covers the complete software pipeline using simulated and dataset-based RF inputs, with physical sensor integration planned as a later phase. SkyGuard demonstrates a low-cost, passive and legally compliant approach to airspace situational awareness for airports, border security, critical infrastructure and public events.

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