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
[1] LIU J,CHEN R,LING P,et al.A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS[J].Sensors,2012,12(12):17208-17233.
[2] DEDES G,DEMPSTER A G.Indoor GPS positioning-challenges and opportunities[C]//IEEE Vehicular Technology Conference.2005.
[3] LI J,WANG X,FENG D,et al.Share in the Commons:Coexistence between LTE Unlicensed and Wi-Fi[J].IEEE Wireless Communications,2016,23(6):16-23.
[4] VO Q D,DE P.A Survey of Fingerprint based Outdoor Localization[J].IEEE Communications Surveys & Tutorials,2015,18(1):491-506.
[5] SHAOCHUAN W,YUZE W,WEN C.A Gossip-based AOADistributed Localization Algorithm for Wireless Sensor Networks[C]//International Symposium on Instrumentation & Measurement.IEEE,2014.
[6] WANG Y,HO V.An Asymptotically Efficient Estimator inClosed-Form for 3D AOA Localization Using a Sensor Network[J].IEEE Transactions on Wireless Communications,2015,14(12):6524-6535.
[7] LI J,JU H,LONG Y,et al.Exploiting multiple access points diversity gain in the multi-access wireless network[C]//2013 IEEE 24th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications(PIMRC).IEEE,2013:1710-1714.
[8] ZHANG W,LIU K,ZHANG W,et al.Deep Neural Networks for Wireless Localization in Indoor and Outdoor Environments[J].Neurocomputing,2016,194:279-287.
[9] SHAO W,LUO H,ZHAO F,et al.Indoor positioning based on fingerprint-imageand deep learning[J].IEEE Access,2018,6:74699-74712.
[10] “Google Goggles.” [OL].Available:http://www.google.com/mobile/goggles/.
[11] SCHROTH G,HUITL R,CHEN D,et al.Mobile Visual Location Recognition[J].IEEE Signal Processing Magazine,2011,28(4):77-89.
[12] ZHU X,LI Q,CHEN G.APT:Accurate outdoor pedestriantracking with smartphones[C]// INFOCOM,2013 Proceedings IEEE.IEEE,2013.
[13] BELMONTE-HERNANDEZ A,HERNANDEZ-PENALOZAG,GUTIERREZ D M,et al.SWiBluX:Multi-Sensor Deep Learning Fingerprint for precise real-time indoor tracking[J].IEEE Sensors Journal,2019,19(9):3473-3486.
[14] YE X,YIN X,CAI X,et al.Neural-network-assisted UE Localization Using Radio-channel Fingerprints in LTE Networks[J].IEEE Access,2017,5:12071-12087.
[15] YIU S,DASHTI M,CLAUSSEN H,et al.Wireless RSSI fingerprinting localization[J].Signal Processing,2016,131:235-244.
[16] WANG X,GAO L,MAO S,et al.CSI-based Fingerprinting for Indoor Localization:A Deep Learning Approach[J].IEEE Transactions on Vehicular Technology,2016,66(1):763-776.
[17] XING H,ZHANG G,SHANG M.Deep Learning[J].International Journal of Semantic Computing,2016,10(3):417-439.
[18] ZHONG J,YANG B,LI Y,et al.Image Fusion and Super-Resolution with Convolutional Neural Network[C]//Chinese Conference on Pattern Recognition.Springer,Singapore,2016:78-88.
[19] JÜRGEN S.Deep learning in neural networks:An overview[J].Neural Networks,2015,61:85-117.
[20] LU J,BEHBOOD V N,HAO P,et al.Transfer learning usingcomputational intelligence:A survey[J].Knowledge-Based Systems,2015,80:14-23.
[21] HE K,ZHANG X,REN S,et al.Identity Mappings in Deep Residual Networks[C]//European Conference on Computer Vision.Cham:Springer,2016:630-645.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[3] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[5] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[8] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[10] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[11] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[12] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[13] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[14] LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[15] SUN Fu-quan, CUI Zhi-qing, ZOU Peng, ZHANG Kun. Brain Tumor Segmentation Algorithm Based on Multi-scale Features [J]. Computer Science, 2022, 49(6A): 12-16.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!