A novel indoor localization method using passive phase difference fingerprinting based on channel state information

被引:14
作者
Dang, Xiaochao [1 ,2 ]
Ren, Jiaju [1 ]
Hao, Zhanjun [1 ,2 ]
Hei, Yili [1 ]
Tang, Xuhao [1 ]
Yan, Yan [1 ]
机构
[1] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Gansu, Peoples R China
[2] Gansu Prov Internet Things Engn Res Ctr, Lanzhou, Gansu, Peoples R China
基金
中国国家自然科学基金;
关键词
Channel state information; principal component analysis; phase difference correction; indoor fingerprint localization; back-propagation neural network;
D O I
10.1177/1550147719844099
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The device-free channel state information indoor fingerprint localization method may lead to phase offset errors, strong fingerprint noise and low sampling classification accuracy. In light of these characteristics, this article presents an indoor localization algorithm that is based on phase difference processing and principal component analysis. First, during the offline phase, this algorithm calculates phase differences to correct for random phase shifts and random time shifts in communication links. Second, the principal component analysis method is used to reduce the dimensionality of the denoised data and establish a robust fingerprint database. During the online phase, the algorithm trains a back-propagation neural network using the fingerprint data and determines the modelled mapping relationship between the fingerprint data and the physical localization after carrying out the phase difference correction and the principal component analysis-based dimensionality reduction. The experiments show that compared with existing fingerprint location methods, this algorithm has the advantages of significant denoising effectiveness and high localization accuracy.
引用
收藏
页数:14
相关论文
共 32 条
  • [21] Wang XY, 2015, IEEE WCNC, P1666, DOI 10.1109/WCNC.2015.7127718
  • [22] A Graphical Model Approach for Efficient Geomagnetism-Pedometer Indoor Localization
    Wu, Hang
    He, Suining
    Chan, S. -H. Gary
    [J]. 2017 IEEE 14TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS), 2017, : 371 - 379
  • [23] CSI-Based Indoor Localization
    Wu, Kaishun
    Xiao, Jiang
    Yi, Youwen
    Chen, Dihu
    Luo, Xiaonan
    Ni, Lionel M.
    [J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2013, 24 (07) : 1300 - 1309
  • [24] A Time-Reversal Paradigm for Indoor Positioning System
    Wu, Zhung-Han
    Han, Yi
    Chen, Yan
    Liu, K. J. R.
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2015, 64 (04) : 1331 - 1339
  • [25] Xiao Jun., 2012, Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific, P1
  • [26] Accelerating Crowdsourcing based Indoor Localization using CSI
    Xie, Haijiang
    Lin, Li
    Jiang, Zhiping
    Xi, Wei
    Zhao, Kun
    Ding, Meiyong
    Zhao, Jizhong
    [J]. 2015 IEEE 21ST INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2015, : 274 - 281
  • [27] MaLoc: A Practical Magnetic Fingerprinting Approach to Indoor Localization using Smartphones
    Xie, Hongwei
    Gu, Tao
    Tao, Xianping
    Ye, Haibo
    Lv, Jian
    [J]. UBICOMP'14: PROCEEDINGS OF THE 2014 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, 2014, : 243 - 253
  • [28] Precise Power Delay Profiling with Commodity Wi-Fi
    Xie, Yaxiong
    Li, Zhenjiang
    Li, Mo
    [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2019, 18 (06) : 1342 - 1355
  • [29] Robust Biomechanical Model-Based 3-D Indoor Localization and Tracking Method Using UWB and IMU
    Yoon, Paul K.
    Zihajehzadeh, Shaghayegh
    Kang, Bong-Soo
    Park, Edward J.
    [J]. IEEE SENSORS JOURNAL, 2017, 17 (04) : 1084 - 1096
  • [30] Real-Time Locating Systems Using Active RFID for Internet of Things
    Zhang, Daqiang
    Yang, Laurence Tianruo
    Chen, Min
    Zhao, Shengjie
    Guo, Minyi
    Zhang, Yin
    [J]. IEEE SYSTEMS JOURNAL, 2016, 10 (03): : 1226 - 1235