计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 404-409.doi: 10.11896/jsjkx.200700170

• 网络&通信 • 上一篇    下一篇

基于LTE网络的室外指纹定位

李达, 雷迎科, 张海川   

  1. 国防科技大学电子对抗学院 合肥230000
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 雷迎科(506293935@qq.com)
  • 作者简介:gfkdlida@163.com

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.

摘要: 由于在复杂环境中可以取得良好的定位效果,基于指纹的定位技术一直是研究的热点。通过利用长期演进(Long Term Evolution,LTE)网络,一种基于深度学习的指纹定位方法被提出用来构建良好的定位系统。受到计算机视觉技术的启发,带有地理位置标记的信号指纹被转化为灰度图片然后进行定位,并以最终构建好的灰度图片数据集的分类准确率来表示定位的准确率。文中采用了一种两级分步训练的方法来实现深度神经网络(Deep Neural Network,DNN)的分类识别。首先,利用深度残差网络(Deep Residual Network,Resnet)对指纹库进行预训练并得到粗糙定位模型,然后利用基于反向传播神经网络(Back Propagation Neural Network,BPNN)的迁移学习算法进一步提取信号特征并得到精确定位模型。实验在真实室外环境下进行,且实验结果表明提出的定位系统可以在室外环境下取得较高精度的定位效果。

关键词: LTE网络, 迁移学习, 深度学习, 室外定位, 指纹定位

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

中图分类号: 

  • 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] 饶志双, 贾真, 张凡, 李天瑞.
基于Key-Value关联记忆网络的知识图谱问答方法
Key-Value Relational Memory Networks for Question Answering over Knowledge Graph
计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[2] 汤凌韬, 王迪, 张鲁飞, 刘盛云.
基于安全多方计算和差分隐私的联邦学习方案
Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy
计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108
[3] 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺.
时序知识图谱表示学习
Temporal Knowledge Graph Representation Learning
计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[4] 方义秋, 张震坤, 葛君伟.
基于自注意力机制和迁移学习的跨领域推荐算法
Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning
计算机科学, 2022, 49(8): 70-77. https://doi.org/10.11896/jsjkx.210600011
[5] 孙奇, 吉根林, 张杰.
基于非局部注意力生成对抗网络的视频异常事件检测方法
Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection
计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061
[6] 王剑, 彭雨琦, 赵宇斐, 杨健.
基于深度学习的社交网络舆情信息抽取方法综述
Survey of Social Network Public Opinion Information Extraction Based on Deep Learning
计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[7] 郝志荣, 陈龙, 黄嘉成.
面向文本分类的类别区分式通用对抗攻击方法
Class Discriminative Universal Adversarial Attack for Text Classification
计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[8] 姜梦函, 李邵梅, 郑洪浩, 张建朋.
基于改进位置编码的谣言检测模型
Rumor Detection Model Based on Improved Position Embedding
计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046
[9] 胡艳羽, 赵龙, 董祥军.
一种用于癌症分类的两阶段深度特征选择提取算法
Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification
计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092
[10] 程成, 降爱莲.
基于多路径特征提取的实时语义分割方法
Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction
计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157
[11] 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木.
中文预训练模型研究进展
Advances in Chinese Pre-training Models
计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018
[12] 周慧, 施皓晨, 屠要峰, 黄圣君.
基于主动采样的深度鲁棒神经网络学习
Robust Deep Neural Network Learning Based on Active Sampling
计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044
[13] 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫.
小样本雷达辐射源识别的深度学习方法综述
Survey of Deep Learning for Radar Emitter Identification Based on Small Sample
计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138
[14] 王君锋, 刘凡, 杨赛, 吕坦悦, 陈峙宇, 许峰.
基于多源迁移学习的大坝裂缝检测
Dam Crack Detection Based on Multi-source Transfer Learning
计算机科学, 2022, 49(6A): 319-324. https://doi.org/10.11896/jsjkx.210500124
[15] 楚玉春, 龚航, 王学芳, 刘培顺.
基于YOLOv4的目标检测知识蒸馏算法研究
Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4
计算机科学, 2022, 49(6A): 337-344. https://doi.org/10.11896/jsjkx.210600204
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!