Advanced Search
Volume 43 Issue 9
Sep.  2021
Turn off MathJax
Article Contents
Xiaohui LI, Kun FANG, Tao FAN, Jiawen LIU, Siting LÜ. Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2601-2607. doi: 10.11999/JEIT200725
Citation: Xiaohui LI, Kun FANG, Tao FAN, Jiawen LIU, Siting LÜ. Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2601-2607. doi: 10.11999/JEIT200725

Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines

doi: 10.11999/JEIT200725
  • Received Date: 2020-08-14
  • Rev Recd Date: 2021-07-02
  • Available Online: 2021-07-15
  • Publish Date: 2021-09-16
  • In order to solve the problem that it is difficult to extract the Unmanned Aerial Vehicle (UAV) positioning signal from the environment with severe multipath interference in the passive positioning of the UAV, a UAV positioning signal separation based on Support Vector Machines (SVM) algorithm is proposed. During the training of the SVM model, the information entropy is obtained by calculating the Euclidean distance between the adjacent data sets of the UAV, and the model data is provided for the SVM to map the high-dimensional space. On this basis, the soft boundary of the threshold of the mapping function is added to make the model have the ability to adjust parameters adaptively to adapt to the data difference caused by the flexible movement of the UAV. Finally, an observer operating characteristic curve is constructed to obtain the result of UAV positioning signal separation. The simulation results show that the proposed algorithm can effectively separate the UAV positioning signal and noise.
  • loading
  • [1]
    QU Yaohong, WU Jizhi, XIAO Bing, et al. A fault-tolerant cooperative positioning approach for multiple UAVs[J]. IEEE Access, 2017, 5: 15630–15640. doi: 10.1109/ACCESS.2017.2731425
    [2]
    GALKIN B, KIBIŁDA J, and DASILVA L A. A stochastic model for UAV networks positioned above demand hotspots in urban environments[J]. IEEE Transactions on Vehicular Technology, 2019, 68(7): 6985–6996. doi: 10.1109/TVT.2019.2916429
    [3]
    PENG Jing, ZHANG Ping, ZHENG Lanxiang, et al. UAV positioning based on multi-sensor fusion[J]. IEEE Access, 2020, 8: 34455–34467. doi: 10.1109/ACCESS.2020.2974285
    [4]
    ZHU Yuhong, MA Tengfei, LI Zhijun, et al. NLOS identification and correction based on multidimensional scaling and quasi-accurate detection[J]. IEEE Access, 2019, 7: 53977–53987. doi: 10.1109/ACCESS.2019.2906866
    [5]
    WU Shixun, ZHANG Shengjun, and HUANG Darong. A TOA-based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination[J]. IEEE Sensors Letters, 2019, 3(3): 1–4. doi: 10.1109/LSENS.2019.2897924
    [6]
    GUO Qing, KE Wei, and TANG Wanchun. Wireless positioning method based on dynamic objective function under mixed LOS/NLOS conditions[C]. 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), Wuhan, China, 2018: 1–4. doi: 10.1109/UPINLBS.2018.8559838.
    [7]
    YU Xiaohan, CHEN Xiaolong, HUANG Yong, et al. Radar moving target detection in clutter background via adaptive dual-threshold sparse Fourier transform[J]. IEEE Access, 2019, 7: 58200–58211. doi: 10.1109/ACCESS.2019.2914232
    [8]
    马济通, 邱天爽, 李蓉, 等. 脉冲噪声下基于Renyi熵的分数低阶双模盲均衡算法[J]. 电子与信息学报, 2018, 40(2): 378–385. doi: 10.11999/JEIT170366

    MA Jitong, QIU Tianshuang, LI Rong, et al. Dual-mode blind equalization algorithm based on Renyi entropy and fractional lower order statistics under impulsive noise[J]. Journal of Electronics &Information Technology, 2018, 40(2): 378–385. doi: 10.11999/JEIT170366
    [9]
    蔡睿妍, 杨力, 钱杨. 脉冲噪声下基于相关熵的相干分布源DOA估计新方法[J]. 电子与信息学报, 2020, 42(11): 2600–2606. doi: 10.11999/JEIT200325

    CAI Ruiyan, YANG Li, and QIAN Yang. A novel DOA estimation method for coherently distributed sources based on correntropy in the impulsive noise[J]. Journal of Electronics &Information Technology, 2020, 42(11): 2600–2606. doi: 10.11999/JEIT200325
    [10]
    ZHAO Yongsheng, HU Dexiu, Zhao Yongjun, et al. Multipath TDOA and FDOA estimation in passive Bistatic radar via multiple signal classification[C]. The 20th International Radar Symposium (IRS), Ulm, Germany, 2019: 1–6. doi: 10.23919/IRS.2019.8768128.
    [11]
    CHENG Lan, WANG Kai, REN M F, et al. Comprehensive analysis of multipath estimation algorithms in the framework of information theoretic learning[J]. IEEE Access, 2018, 6: 5521–5530. doi: 10.1109/ACCESS.2018.2793896
    [12]
    KHAN U, YE Yunxing, AISHA A U, et al. Precision of EM simulation based wireless location estimation in multi-sensor capsule endoscopy[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2018, 6: 1–11. doi: 10.1109/JTEHM.2018.2818177
    [13]
    WANG Boyuan, GAN Xingli, LIU Xuelin, et al. A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning[J]. IEEE Access, 2020, 8: 30591–30602. doi: 10.1109/ACCESS.2020.2973212
    [14]
    KHOUZANI M and MALACARIA P. Optimal channel design: A game theoretical analysis[J]. Entropy, 2018, 20(9): 675. doi: 10.3390/e20090675
    [15]
    ZHOU Di, ZHUANG Xiao, ZUO Hongfu, et al. Deep learning-based approach for civil aircraft hazard identification and prediction[J]. IEEE Access, 2020, 8: 103665–103683. doi: 10.1109/ACCESS.2020.2997371
    [16]
    SUN Xiankun, LIU Lan, LI Chengfan, et al. Classification for remote sensing data with improved CNN-SVM method[J]. IEEE Access, 2019, 7: 164507–164516. doi: 10.1109/ACCESS.2019.2952946
    [17]
    BAO Jianrong, NIE Jianyuan, LIU Chao, et al. Improved blind spectrum sensing by covariance matrix Cholesky decomposition and RBF-SVM decision classification at low SNRs[J]. IEEE Access, 2019, 7: 97117–97129. doi: 10.1109/ACCESS.2019.2929316
    [18]
    WU Xiaohe, ZUO Wangmeng, LIN Liang, et al. F-SVM: Combination of feature transformation and SVM learning via convex relaxation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11): 5185–5199. doi: 10.1109/TNNLS.2018.2791507
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(3)

    Article Metrics

    Article views (964) PDF downloads(139) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return