Efficient WiFi Fingerprint Training Using Semi-supervised Learning

被引:0
|
作者
Yuan, Ye [1 ]
Pei, Ling [1 ]
Xu, Changqing [1 ]
Liu, Qianchen [1 ]
Gu, Tingyu [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai Key Lab Nav & Locat Based Serv, Shanghai 200240, Peoples R China
关键词
fingerprint; indoor localization; continuously sampling; semi-supervise learning; Gaussian Processes;
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
Fingerfrinting based WiFi positioning approach needs an off-line training phase to build a radio map with received signal strength indication vector of each reference point. In existing systems, this training phase may cost a tremendous amount of workload to achieve satisfying location result. To cut down on the workload notably and guarantee the location result in the meantime, we will introduce an efficient WiFi fingerprint training method: Fa-Fi namely fast fingerprint generation, which uses semi-supervised learning in this article. This proposed method can reduce the training phase time cost to about 1/5, and guarantee the localization accuracy at the same time.
引用
收藏
页码:148 / 155
页数:8
相关论文
共 50 条
  • [1] Graph-based Efficient WiFi Fingerprint Training Using Un-supervised Learning
    Zhao, Bo
    Pei, Ling
    Xu, Changqing
    Gu, Li
    PROCEEDINGS OF THE 28TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS+ 2015), 2015, : 2301 - 2310
  • [2] Generative Adversarial Training for Supervised and Semi-supervised Learning
    Wang, Xianmin
    Li, Jing
    Liu, Qi
    Zhao, Wenpeng
    Li, Zuoyong
    Wang, Wenhao
    FRONTIERS IN NEUROROBOTICS, 2021, 15
  • [3] Manifold adversarial training for supervised and semi-supervised learning
    Zhang, Shufei
    Huang, Kaizhu
    Zhu, Jianke
    Liu, Yang
    NEURAL NETWORKS, 2021, 140 : 282 - 293
  • [4] On the Learning Dynamics of Semi-Supervised Training for ASR
    Wallington, Electra
    Kershenbaum, Benji
    Klejch, Ondrej
    Bell, Peter
    INTERSPEECH 2021, 2021, : 716 - 720
  • [5] Interpolation consistency training for semi-supervised learning
    Verma, Vikas
    Kawaguchi, Kenji
    Lamb, Alex
    Kannala, Juho
    Solin, Arno
    Bengio, Yoshua
    Lopez-Paz, David
    NEURAL NETWORKS, 2022, 145 : 90 - 106
  • [6] Interpolation Consistency Training for Semi-Supervised Learning
    Verma, Vikas
    Lamb, Alex
    Kannala, Juho
    Bengio, Yoshua
    Lopez-Paz, David
    PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2019, : 3635 - 3641
  • [7] MarginGAN: Adversarial Training in Semi-Supervised Learning
    Dong, Jinhao
    Lin, Tong
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [8] An efficient spatial semi-supervised learning algorithm
    Vatsavai, Ranga Raju
    Shekhar, Shashi
    Burk, Thomas E.
    INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS, 2007, 22 (06) : 427 - 437
  • [9] LABEL REUSE FOR EFFICIENT SEMI-SUPERVISED LEARNING
    Hsieh, Tsung-Hung
    Chen, Jun-Cheng
    Chen, Chu-Song
    2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 3697 - 3701
  • [10] Semi-supervised learning using adversarial training with good and bad samples
    Li, Wenyuan
    Wang, Zichen
    Yue, Yuguang
    Li, Jiayun
    Speier, William
    Zhou, Mingyuan
    Arnold, Corey
    MACHINE VISION AND APPLICATIONS, 2020, 31 (06)