UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Abstract

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

“…[25] Review on UAV-based communications using ML, focusing on resource management, channel modeling, location, and security up to the year 2019. [26] Review of the technical classification and implementation methods of UAV detection and tracking in urban IoT environment and provided a limited number of references covering up to the year 2023. [27] Survey on DL-based UAV detection, with a focus on radar technologies up to the year 2021.…”

Design of LABVIEW-based UAV Online Monitoring and Security Situation Assessment System

“…s a kind of flying machine, since its invention in the 1940s, unmanned aerial vehicle (UAV) has undergone decades of development, and now it has been integrated into all walks of national production and life [1]- [4], and has been widely used in many fields such as military, civil, and commercial. On the military side, it performs a variety of military tasks in war, including reconnaissance, target location, signal intelligence search, defense and control fire decoys and other real-time intelligence [5]- [9].…”

Evaluation of the Success of Simulation of the Unmanned Aerial Vehicle Precision Landing Provided by a Newly Designed System for Precision Landing in a Mountainous Area

“…The use of the acoustic spectrum to detect the presence of the unmanned aerial vehicle in the monitored area is familiar. Such detection of the presence of UAVs primarily fulfils a security purpose, and it serves to protect the fauna, flora, health and property of the state, legal entities and natural persons, as well as to ensure a safe flight area in the monitored area [14][15][16]. The disadvantage of using the acoustic spectrum in the mountains is the high refraction of the signal from surrounding obstacles (rocks, trees and other objects).…”