A method indoor Multi-path IR-UWB Location Based on Multi-Task Compressive Sensing

被引:0
|
作者
Li, Xiaofei [1 ,2 ,3 ]
He, Di [4 ]
Jiang, Lingge [5 ,6 ]
Yu, WenSheng [7 ]
Chen, Xiaohua [8 ]
机构
[1] WuYi Univ, Coll Math & Comp, Nanping, Fujian, Peoples R China
[2] East China Normal Univ, Shanghai Key Lab Trustworthy Comp, Shanghai, Peoples R China
[3] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing, Jiangsu, Peoples R China
[4] Shanghai Jiao Tong Univ, Shanghai Key Lab Nav & Locat Based Serv, Shanghai, Peoples R China
[5] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai, Peoples R China
[6] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing, Jiangsu, Peoples R China
[7] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China
[8] HuZhou Univ, Sch Informat & Engn, Huzhou, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the paper, the ultra-wideband (UWB) localization method based on multi-task compressive sensing (MT-CS) is proposed to solve the UWB localization accuracy performance problem. In the indoor multi-path NLOS environment, the transmission channel pulse response (CIR) is estimated precisely by use of the MT CS algorithm, and least square error (LSE) algorithm are calculated to estimate the target location. The simulation results illustrate that the MT-CS algorithm compared with the BCS algorithm can estimate the localization more precisely, and approximate the CRLB algorithm. Meanwhile, the MT-CS algorithm possesses advantage to apply in the UWB location.
引用
收藏
页码:259 / 263
页数:5
相关论文
共 50 条
  • [31] A Web Service Composition Method Based on Multi-path
    Dong, Jian
    2009 IITA INTERNATIONAL CONFERENCE ON SERVICES SCIENCE, MANAGEMENT AND ENGINEERING, PROCEEDINGS, 2009, : 430 - 432
  • [32] Multi-Classification Algorithm for Human Motion Recognition Based on IR-UWB Radar
    Qi, Rui
    Li, Xiuping
    Zhang, Yi
    Li, Yubing
    IEEE SENSORS JOURNAL, 2020, 20 (21) : 12848 - 12858
  • [33] A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar
    Dang, Xiaochao
    Zhang, Jinlong
    Hao, Zhanjun
    SENSORS, 2022, 22 (16)
  • [34] Multi-task Bayesian compressive sensing for vibration signals in diesel engine health monitoring
    Wang Qiang
    Zhang Peilin
    Meng Chen
    Wang Huaiguang
    Wang Cheng
    MEASUREMENT, 2019, 136 : 625 - 635
  • [35] Multi-Task Compressive Sensing of Vibration Signal using GMM Clustering for Wireless Transmission
    Ma, Yun-Fei
    Bai, Hua-Jun
    Jia, Xi-Sheng
    Wang, Guang-Long
    Guo, Chi-Ming
    2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-QINGDAO), 2019,
  • [36] Multi-task textile stereoscopic warehouse location allocation and task sequence optimization method
    Chen L.
    Cheng J.
    Zhu Y.
    Xu H.
    Wang Y.
    Tao F.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2023, 29 (04): : 1371 - 1385
  • [37] Wideband Spectrum Sensing based on Collaborative Multi-Task Learning
    Zhang, Weishan
    Wang, Yue
    Yu, Fuxun
    Qin, Zhuwei
    Chen, Xiang
    Tian, Zhi
    2022 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), 2022, : 693 - 698
  • [38] Real-Time Indoor Layout Estimation Method Based on Multi-Task Supervised Learning
    Huang Rongze
    Meng Qinghao
    Liu Yinbo
    LASER & OPTOELECTRONICS PROGRESS, 2021, 58 (14)
  • [39] Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach
    Salucci, Marco
    Anselmi, Nicola
    JOURNAL OF IMAGING, 2021, 7 (11)
  • [40] Moving Target Tracking Based on Multi-view Fusion Using IR-UWB Radar
    Chen W.-Y.
    Zhang F.-S.
    Liu J.-J.
    Bao P.
    Zhang D.-Q.
    Ruan Jian Xue Bao/Journal of Software, 2023, 34 (12): : 5457 - 5476