Mobile Communication Base Station Antenna Measurement Using Unmanned Aerial Vehicle

被引:6
作者
Zhai, Yikui [1 ,2 ]
Ke, Qirui [1 ]
Xu, Ying [1 ]
Deng, Wenbo [1 ]
Gan, Junying [1 ]
Zeng, Junying [1 ]
Zhou, Wenlve [1 ]
Scott, Fabio [2 ]
Labati, Ruggero Donida [2 ]
Piuri, Vincenzo [2 ]
机构
[1] Wuyi Univ, Dept Intelligent Mfg, Jiangmen 529020, Peoples R China
[2] Univ Milan, Dept Informat, I-20133 Milan, Italy
基金
中国国家自然科学基金;
关键词
UAV; antenna measurement; instance segmentation; least squares; OBJECT DETECTION;
D O I
10.1109/ACCESS.2019.2935613
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traditional base station antenna measurement methods conducted with professional worker climbing towers tend to raise safety and inefficiency concerns in practical application. Designed to address the above problems, this paper proposes an intelligent and fully automatic antenna measurement unmanned aerial vehicle (UAV) system for mobile communication base station. Firstly, an antenna database, containing 19,715 images, named UAV-Antenna is constructed by image capturing with the help of UAVs flying around various base stations. Secondly, Mask R-CNN is adopted to train an optimal instance segmentation model on UAV-Antenna. Then, pixel coordinates and threshold are utilized for measuring antenna quantity and separate all antenna data for further measuring. Finally, a least squares method is employed for measuring antenna parameters. Experimental results show that the proposed method can not only satisfy the industry application standards, but also guarantee safety of labors and efficiency of performance.
引用
收藏
页码:119892 / 119903
页数:12
相关论文
共 34 条
  • [1] [Anonymous], IEEE T NEURAL NETW L
  • [2] Berzaghi P, 2019, P 18 INT C NEAR INF, P51
  • [3] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
    Chen, Liang-Chieh
    Papandreou, George
    Kokkinos, Iasonas
    Murphy, Kevin
    Yuille, Alan L.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) : 834 - 848
  • [4] Chen Liang-Chieh, 2018, ECCV, P801, DOI [DOI 10.1007/978-3-030-01234-249, 10.1007/978-3-030-01234-2_49]
  • [5] Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
    Cheng, Gong
    Han, Junwei
    Zhou, Peicheng
    Xu, Dong
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 28 (01) : 265 - 278
  • [6] Ciocca G., 2019, LECT NOTES COMPUTER
  • [7] Deng L, 2014, Foundations and Trends in Signal Processing: DEEP LEARNING-Methods and Applications, DOI [DOI 10.1561/2000000039, 10.1561/]
  • [8] Deep Learning Based Communication Over the Air
    Doerner, Sebastian
    Cammerer, Sebastian
    Hoydis, Jakob
    ten Brink, Stephan
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2018, 12 (01) : 132 - 143
  • [9] On the Use of Unmanned Aerial Vehicles for Antenna and Coverage Diagnostics in Mobile Networks
    Garcia Fernandez, Maria
    Alvarez Lopez, Yuri
    Las-Heras Andres, Fernando
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (07) : 72 - 78
  • [10] Garcia-Fernandez M., 2018, P EUR C ANT PROP, P794