Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 404-409.doi: 10.11896/jsjkx.200700170

• Network & Communication • Previous Articles     Next Articles

Outdoor Fingerprint Positioning Based on LTE Networks

LI Da, LEI Ying-ke, ZHANG Hai-chuan   

  1. College of Electronic Engineering,National University of Defense Technology,Hefei 230000,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:LI Da,Ph.D.His main research inte-rests includesignal processing,neural networks,and machine learning.
    LEI Ying-ke,Ph.D professor.His main research interests include neural network,location-based services,and communication radiation source identification.

Abstract: Owing to the satisfactory positioning accuracy in complex environments,fingerprint-based positioning technology has always been a hot topic of research.By leveraging long term evolution (LTE) signals,a deep learning based outdoor fingerprint positioning method is proposed to construct a positioning system.Inspiring by the computer vision technology,the geo-tag signals are converted into gray scale images for positioning.The positioning accuracy is expressed by the classification accuracy of the constructed gray image dataset.In this paper,a two-level training architecture is developed to realize the classification of deep neural network (DNN).First,a deep residual network (Resnet) is used to pre-train the fingerprint database and obtain a rough positioning model.Then,a transfer learning algorithm based on back propagation neural network (BPNN) is used to further extract signal features and obtain an accurate positioning model.The experiment is conducted in a real outdoor environment,and the experiment results show that the proposed positioning system can achieve a satisfactory positioning accuracy in complex environments.

Key words: Deep learning, Fingerprint positioning, LTE signal, Outdoor positioning, Transfer learning

CLC Number: 

  • TP391.4
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