Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review
Author:
Seidaliyeva Ulzhalgas1ORCID, Ilipbayeva Lyazzat2, Taissariyeva Kyrmyzy1, Smailov Nurzhigit1ORCID, Matson Eric T.3
Affiliation:
1. Department of Electronics, Telecommunications and Space Technologies, Satbayev University, Almaty 050013, Kazakhstan 2. Department of Radio Engineering, Electronics and Telecommunications, International IT University, Almaty 050040, Kazakhstan 3. Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907-2021, USA
Abstract
The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.
Funder
Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference90 articles.
1. Samaras, S., Diamantidou, E., Ataloglou, D., Sakellariou, N., Vafeiadis, A., Magoulianitis, V., Lalas, A., Dimou, A., Zarpalas, D., and Votis, K. (2019). Deep Learning on Multi-Sensor Data for Counter UAV Applications—A Systematic Review. Sensors, 19. 2. Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies;Fu;IEEE Access,2021 3. (2021, February 19). Counter Drone Tactics: Which Drones Are a Real Threat, and Which Aren’t?. Available online: Https://www.ifsecglobal.com/drones/counter-drone-tactics-which-drones-are-a-real-threat-and-which-arent/. 4. Survey on Anti-Drone Systems: Components, Designs, and Challenges;Park;IEEE Access,2021 5. (2023, October 25). Worldwide Drone Incidents. Available online: Https://dedrone.com/resources/incidents-new/all?bd17d27c_page=1.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|