Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (1): 29-34.DOI: 10.3778/j.issn.1002-8331.1809-0143

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Indoor Localization Based on Deep Learning Using Wi-Fi and iBeacon

XUE Wei, CHEN Jing, ZHANG Yi   

  1. 1.School of Internet of Thing Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
    2.Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2019-01-01 Published:2019-01-07


薛  伟,陈  璟,张  熠   

  1. 1.江南大学 物联网工程学院,江苏 无锡 214122
    2.江南大学 物联网技术应用教育部工程研究中心,江苏 无锡 214122


Aiming at the problem that the traditional indoor fingerprint localization algorithm has low positioning accuracy and is easily affected by the environment, an indoor
localization algorithm based on deep learning using Wi-Fi and iBeacon is proposed. Signal strength of each AP and iBeacon is collected at each reference point in offline phase, and
is used to train the stacked auto-encoder which is used to extract features from a large number of signal strength samples with noise. These features are used to construct the
fingerprint database. The features of the point to be measured can be obtained by the stacked auto-encoder in online phase. Then these features are matched in fingerprint database.
The position of the point to be measured is estimated by the nearest neighbor algorithm. Experimental results show that the proposed indoor localization algorithm has higher
localization accuracy.

Key words: indoor localization, deep learning, stacked auto-encoder, nearest neighbor algorithm, iBeacon, Wi-Fi



关键词: 室内定位, 深度学习, 堆叠自动编码机, 近邻算法, iBeacon, Wi-Fi