Anti-UAV detection and identification technology: Fundamentals, methods and challenges

Abstract

The rapid development of unmanned aerial vehicle (UAV) technology has driven its widespread application in military, commercial, and civilian domains, such as surveillance, logistics, and disaster response. However, this growing popularity has also raised serious security and privacy concerns, as UAVs can be misused for smuggling, espionage, terrorism, and other illegal activities. As a result, anti-UAV detection and identification technologies have become a critical research area for monitoring, identifying, and mitigating the threats posed by rogue drones. This paper provides a comprehensive review of the latest advancements in anti-UAV detection and identification technologies. To deepen the understanding of these technologies, the paper first introduces the UAV system (UAS) framework and its corresponding anti-UAV systems. Subsequently, it presents an in-depth analysis of the primary detection and identification methods, including those based on image processing, radio frequency (RF) signals, radar, infrared characteristics, acoustic features, and multi-modal fusion. The advantages, limitations, and application scenarios of each method are systematically compared and summarized. Furthermore, this paper identifies the major challenges faced by current anti-UAV detection technologies, such as detection accuracy, range, robustness, and cost, and explores potential directions for future research and development. By synthesizing these insights, this paper aims to provide researchers and practitioners with a comprehensive understanding of the current state of the art in anti-UAV technologies, serving as a valuable reference to advance research progress and foster innovation in this critical field.