Cellular Base Station Imaging for UAV Detection

被引:5
作者
Cao, Pan [1 ]
机构
[1] Univ Hertfordshire, Sch Phys Engn & Comp Sci, Hatfield AL10 9AB, Herts, England
来源
IEEE ACCESS | 2022年 / 10卷
关键词
Radar imaging; Base stations; Imaging; Radar; OFDM; Sensors; Wireless communication; Cellular mobile communication; joint imaging and communication (JIAC); high resolution radar imaging; unmanned aerial vehicles (UAVs) detection; WAVE-FORM DESIGN; RADAR; COMMUNICATION; 5G;
D O I
10.1109/ACCESS.2022.3152534
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the use of unmanned aerial vehicles (UAVs) is greatly increasing, there is an emerging threat of using UAVs in infrastructure/cyber-attacks and data-eavesdropping. From the safety and security perspective, it is a timely need to build an air surveillance system that enables a seamless detection function for low-and-middle altitude flying targets. However, it is unrealistic to widely deploy classical radar stations due to the astronomical cost. Rethinking the role of cellular mobile communication networks, we desire to add a "vision-like" capability to the widely deployed outdoor cellular base stations (BSs) to realize joint imaging and communication (JIAC) simultaneously through sharing the existing cellular communication infrastructure and spectrum. In this work, it is for the first time to systematically study and demonstrate the concept of cellular base station imaging for UAV detection, which allows a cellular BS to work like an inverse synthetic-aperture radar (ISAR) besides communication. Firstly, we provide the JIAC transmission signalling and systematic operation mechanism. Secondly, the feasibility of JIAC is investigated and analysed to support the idea of cellular base station imaging. Finally, numerical simulation evaluates the imaging performance of three typical types of cellular BSs operating at 900 MHz, 3.5 GHz and 28 GHz, respectively, which implies that cellular BS imaging works for UAV detection! Furthermore, the radar imaging function, as a new by-product, requires only a very little change to the current orthogonal frequency-division multiplexing (OFDM) communication signalling and has nearly no influence on the current communication operation and performance.
引用
收藏
页码:24843 / 24851
页数:9
相关论文
共 50 条
  • [41] Impact of Dust and Sandstorms on 6G UAV Base Station Performance in Arid Saudi Arabian Environments
    Shalaby, Abdulrahman M.
    Othman, Noor S.
    Shalaby, Mohamed
    IEEE ACCESS, 2024, 12 : 86194 - 86207
  • [42] Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station
    Mitsui, Shu
    Nishiyama, Hiroki
    IEEE ACCESS, 2023, 11 : 21332 - 21344
  • [43] Multicriteria UAV Base Stations Placement for Disaster Management
    Akram, Tallha
    Awais, Muhammad
    Naqvi, Rameez
    Ahmed, Ashfaq
    Naeem, Muhammad
    IEEE SYSTEMS JOURNAL, 2020, 14 (03): : 3475 - 3482
  • [44] 3D Deployment of Multiple UAV-Mounted Base Stations for UAV Communications
    Zhang, Chen
    Zhang, Leyi
    Zhu, Lipeng
    Zhang, Tao
    Xiao, Zhenyu
    Xia, Xiang-Gen
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2021, 69 (04) : 2473 - 2488
  • [45] Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station
    Wang, Xinyi
    Fei, Zesong
    Zhang, J. Andrew
    Huang, Jingxuan
    IEEE COMMUNICATIONS LETTERS, 2022, 26 (02) : 249 - 253
  • [46] UAV-Aided Cellular Operation by User Offloading
    Ali, Muntadher A.
    Jamalipour, Abbas
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (12): : 9855 - 9864
  • [47] QoS-Compliant 3-D Deployment Optimization Strategy for UAV Base Stations
    Zhong, Xukai
    Huo, Yiming
    Dong, Xiaodai
    Liang, Zhonghua
    IEEE SYSTEMS JOURNAL, 2021, 15 (02): : 1795 - 1803
  • [48] Reinforcement Learning for Joint Detection and Mapping Using Dynamic UAV Networks
    Guerra, Anna
    Guidi, Francesco
    Dardari, Davide
    Djuric, Petar M.
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2024, 60 (03) : 2586 - 2601
  • [49] Simulation and Detection Performance Evaluation of a UAV-mounted Passive Radar
    Vinogradov, Evgenii
    Kovalev, Dmitry A.
    Pollin, Sofie
    2018 IEEE 29TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2018, : 1185 - 1191
  • [50] Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection
    Yang, Yipu
    Yang, Fan
    Sun, Liguo
    Xiang, Ti
    Lv, Pin
    IEEE SENSORS JOURNAL, 2023, 23 (08) : 8639 - 8653