On the Detection of Unauthorized Drones-Techniques and Future Perspectives: A Review

被引:72
作者
Khan, Muhammad Asif [1 ]
Menouar, Hamid [1 ]
Eldeeb, Aisha [1 ]
Abu-Dayya, Adnan [1 ]
Salim, Flora D. [2 ]
机构
[1] Qatar Univ, Qatar Mobil Innovat Ctr QMIC, Doha, Qatar
[2] Univ New South Wales UNSW, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia
关键词
Drones; Sensors; Privacy; Autonomous aerial vehicles; Sensor phenomena and characterization; Wireless sensor networks; Wireless communication; Unmanned aerial vehicles; drone detection; privacy; radio; radar; security; visual; ACTIVITY CLASSIFICATION; UAV DETECTION; LOCALIZATION; TRACKING; IDENTIFICATION; TECHNOLOGIES; SIGNALS; SYSTEMS; SENSOR;
D O I
10.1109/JSEN.2022.3171293
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, cost-effective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.
引用
收藏
页码:11439 / 11455
页数:17
相关论文
共 142 条
[1]  
911security.com, DRONE DETECTION EVER
[2]   Applications, Deployments, and Integration of Internet of Drones (IoD): A Review [J].
Abualigah, Laith ;
Diabat, Ali ;
Sumari, Putra ;
Gandomi, Amir H. .
IEEE SENSORS JOURNAL, 2021, 21 (22) :25532-25546
[3]  
aerialarmor, AERIAL LENS DRONE DE
[4]  
aerodefense.tech, AIRWARDEN DRONE DETE
[5]  
Al-Emadi S, 2019, INT WIREL COMMUN, P459, DOI 10.1109/IWCMC.2019.8766732
[6]   RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database [J].
Al-Sa'd, Mohammad F. ;
Al-Ali, Abdulla ;
Mohamed, Amr ;
Khattab, Tamer ;
Erbad, Aiman .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 100 :86-97
[7]   Sensor Fusion for Drone Detection [J].
Aledhari, Mohammed ;
Razzak, Rehma ;
Parizi, Reza M. ;
Srivastava, Gautam .
2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), 2021,
[8]  
aljazeera.com, DRONE ATTACKS TARGET
[9]   DroneRF dataset: A dataset of drones for RF-based detection, classification and identification [J].
Allahham, M. H. D. Saria ;
Al-Sa'd, Mohammad F. ;
Al-Ali, Abdulla ;
Mohamed, Amr ;
Khattab, Tamer ;
Erbad, Aiman .
DATA IN BRIEF, 2019, 26
[10]   Deep Learning for RF-based Drone Detection and Identification using Welch's Method [J].
Almasri, Mahmoud .
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, TECHNOLOGY AND APPLICATIONS (DATA), 2021, :208-214