Airport Ground-Based Aerial Object Surveillance Technologies for Enhanced Safety: A Systematic Review

Airport airspace safety is increasingly threatened by small, unmanned aircraft systems and wildlife that traditional radar cannot detect. While earlier reviews addressed general counter-UAS techniques, individual sensors, or the detection of single objects such as birds or drones, none has systematically reviewed airport-based, multi-sensor surveillance strategies through a safety-theoretical lens. A systematic review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, synthesized recent research on fixed, ground-based aerial detection capabilities for small aerial hazards, specifically unmanned aircraft systems (sUAS) and avian targets, within operational airport environments. Searches targeted English-language, peer-reviewed articles from 2016 through 2025 in Web of Science and Scopus. Due to methodological heterogeneity across sensor technologies, a narrative synthesis was executed. The review of thirty-six studies, analyzed through Reason’s Swiss Cheese Model and Endsley’s Situational Awareness framework, found that only layered multi-sensor fusion architectures effectively address detection gaps for Low-Slow-Small (LSS) threats. Based on these findings, the review proposes seamless integration with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems through standardized data-exchange interfaces, complemented by theoretically grounded risk-based deployment strategies aligning surveillance technology tiers with operational risk profiles, from basic Remote ID receivers in low-risk rural environments to comprehensive multi-sensor fusion at high-density hubs, major airports, and urban vertiports.