Drone detection in airport environments: a literature review

Abstract

The increasing use of drones in airport airspace presents a serious challenge to safety and efficiency. Incidents involving unmanned aerial vehicles can cause delays, flight cancellations, and collision risks, raising concerns among airport officials, travelers, and other aviation stakeholders. This study aims to systematically analyze the main drone detection techniques used in airports, identifying research gaps, advantages, and limitations of each method while also highlighting future directions to improve airspace security. Kitchenham’s systematic review method was used, with searches carried out from 2014 to 2025. After screening titles and abstracts and applying inclusion criteria, 25 publications were thoroughly assessed. The analysis shows that while radar systems provide the longest detection range (km) and radio frequency methods achieve the highest classification accuracy (99%), they often come with higher costs. In comparison, camera-based systems can reach high precision (90%) at speeds up to 170 FPS, and multimodal solutions show the greatest potential for robustness, with positioning errors below 1.5% of the detection range. Although technical and operational challenges still exist, the combined use of various methods and machine learning techniques shows promise for improving the accuracy and reliability of drone detection at airports.