A Review of Urban Airspace Drone Monitoring and Countermeasure Technologies under the Context of the Low-Altitude Economy
(State-owned Changhong Machinery Factory, Guilin 541000, China)
Abstract
With the rapid development of the low-altitude economy, urban airspace has become increasingly complex, and frequent unauthorized drone flights pose serious challenges to aviation safety and public order. This study focuses on three key technical areas: identification and classification of low-altitude aerial vehicles, multi-source fusion surveillance architecture, and countermeasure technologies. It systematically reviews the latest research progress both domestically and internationally, compares the advantages and disadvantages of various technical approaches, and discusses the technical challenges and policy recommendations in complex urban environments, aiming to provide a reference for low-altitude airspace security management. Furthermore, by integrating drone system security threats, collaborative defense, and graded disposal procedures, this study proposes a technical combination approach tailored to urban scenarios.
Keywords
low-altitude economy; UAV detection; multisensor fusion; counter-UAV; airspace management
References
- [1] Klare J,Biallawons O,Cerutti-Maori D. UAV Detection With MIMO Radar [C]//2017 18th International Radar Symposium (IRS).IEEE,2017:1-8.
- [2] Chen Weishi, Niu Hongchuang, Wang Xin Overview of multi-source fusion detection of flying birds and unmanned aerial vehicles in airport airspace [J]. Acta Aeronautica Sinica, 2025, 46 (10): 31251
- [3] Allahham M S,Al-Sa'd M F,Al-Ali A,et al. DroneRF Dataset: A Dataset of Drones for RF-Based Detection, Classification and Identification [J/OL].Data in Brief,2019,26:104313. https://doi.org/10.1016/j.dib.2019.104275.
- [4] Elsayed M,Reda M,Mashaly A S,et al. Review on Real-Time Drone Detection Based on Visual Band Electro-Optical (EO) Sensor [C]//2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS),2021:57-65.
- [5] Frid A,Ben-Shimol Y,Manor E,et al. Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks [J/OL].Sensors,2024,24(8):2427. https://doi.org/10.3390/s24082427.
- [6] Sokmen A Z,Canbolat H. Counter-UAV Systems [J].Global Journal of Engineering and Technology,2023,2(8):32-36.
- [7] Xing L,Cui C,Wang M,et al. From Detection to Countermeasure:A Review of Key Technologies in Counter-Unmanned Aerial Vehicle Systems [J/OL].IET Cyber-Systems and Robotics,2026,8:e70048. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/csy2.70048.
- [8] Samaras S,Diamantidou E,Ataloglou D,et al. Deep Learning on Multi Sensor Data for Counter UAV Applications: A Systematic Review [J/OL].Sensors,2019,19(22):4837. https://doi.org/10.3390/s19224837.
- [9] Ge Z,Liu S,Wang F,et al. YOLOX: Exceeding YOLO Series in 2021 [PP/OL]. https://arxiv.org/abs/2107.08430.
- [10] Carion N,Massa F,Synnaeve G,et al. End-to-End Object Detection with Transformers [C]//Computer Vision—ECCV 2020,2020:213-229.
- [11] He X,Fan K,Xu Z. UAV Identification Based on Improved YOLOv7 Under Foggy Condition [J].Signal, Image and Video Processing,2024,18:6173-6183.
- [12] Yasmeen A,Daescu O. Recent Research Progress on Ground-to-Air Vision-Based Anti-UAV Detection and Tracking Methodologies: A Review [J/OL].Drones,2025,9(1):58. https://doi.org/10.3390/drones9010058.
- [13] Gao C,Zhou T,Wang B,et al. Research on Anti Drone Swarm Technology Based on Complex Urban Environment [C]//2023 International Conference on Network,Multimedia and Information Technology (NMITCON),2023:1-6.
- [14] Allahham M S,Khattab T,Mohamed A. Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach [C]//2020 IEEE International Conference on Informatics,IoT,and Enabling Technologies (ICIoT),2020:112-117.
- [15] Qiu Baohua Overview of Anti UAV Technology: Integration of Communication Technology and Artificial Intelligence [J]. ZTE Technology, 2024, 30 (2): 89-99
- [16] Xu L,Luo Z. Anti-UAV Detection and Identification Technology:Fundamentals,Methods and Challenges [J/OL].Physical Communication,2025,71:102676. https://doi.org/10.1016/j.phycom.2025.102676.
- [17] Bender C. DJI Drone IDs Are Not Encrypted [PP/OL].https://arxiv.org/abs/2207.10795.
- [18] Hussain M,Khan A,Liaquat M,et al. A Comprehensive Review on Deep Learning-Based Data Fusion [J].IEEE Access,2024,12:180093-180124.
- [19] Svanström F,Alonso-Fernandez F,Englund C. Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities [J/OL].Drones,2022,6(11):317.https://doi.org/10.3390/drones6110317.
- [20] Liu F,Cui Y,Masouros C,et al. Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond [J].IEEE Journal on Selected Areas in Communications,2022,40(6):1728-1767.
- [21] Jopanya P,Osorio D P M. Utilizing 5G NR SSB Blocks for Passive Detection and Localization of Low-Altitude Drones [PP/OL].https://arxiv.org/abs/2504.02641.
- [22] Capitán C,León H,Capitán J,et al. Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services [J/OL].Applied Sciences,2021,11(9):3995.https://doi.org/10.3390/app11093995.
- [23] Kopardekar P,et al. A Survey on the Unmanned Aircraft System Traffic Management [J].ACM Computing Surveys,2023,56(3):1-39.
- [24] Castrillo V U,Manco A,Pascarella D,et al. A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones [J/OL].Drones,2022,6(3):65. https://doi.org/10.3390/drones6030065.
- [25] Talaei K T,Ismail S,Kaabouch N. Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs [J/OL].Sensors,2022,22(2):662. https://doi.org/10.3390/s22020662.
- [26] Chamola V,Kotesh P,Agarwal A,et al. A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques [J/OL].Ad Hoc Networks,2021,111:102324.https://doi.org/10.1016/j.adhoc.2020.102324.
- [27] Wei X,Ma J,Sun C. A Survey on Security of Unmanned Aerial Vehicle Systems: Attacks and Countermeasures [J].IEEE Internet of Things Journal,2024,11(21):34826-34847.
About the authors
Tan Shunyao (1989-), male, Han ethnicity, from Guilin, Guangxi, engineer, bachelor's degree, research direction: unmanned aerial vehicle technology; Wang Haichuan (1998-), male, Han ethnicity, from Guilin, Guangxi, China, none, master's degree, research direction: radar signal processing; Corresponding author: Ning Cheng (1990-), male, Han ethnicity, from Guilin, Guangxi, engineer, bachelor's degree, research direction: radar signal processing; Zhang Yongming (1981-), male, Han ethnicity, from Guilin, Guangxi, is a senior engineer with a bachelor's degree. His research direction is radar signal processing.