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Indoor Positioning Based on Fingerprint-Image and Deep Learning

文献类型：期刊论文

作者Zhao, Fang6; Crivello, Antonino1; Zhao, Zhongliang2; Ma, Yan5; Shao, Wenhua5,6; Luo, Haiyong3,4
刊名IEEE ACCESS
出版日期2018
卷号6页码:74699-74712
关键词Indoor positioning indoor localization neural networks fingerprint feature extraction
ISSN号2169-3536
DOI10.1109/ACCESS.2018.2884193
英文摘要Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns.
资助项目National Key Research and Development Program[2018YFB0505200] ; National Natural Science Foundation of China[61872046] ; BUPT Excellent Ph.D. Students Foundation[CX2017404] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000454390300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/3484]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhao, Zhongliang; Luo, Haiyong
作者单位1.CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy
2.Univ Bern, Inst Comp Sci, CH-3012 Bern, Switzerland
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China
5.Beijing Univ Posts & Telecommun, Inst Network Technol, Beijing 100876, Peoples R China
6.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Fang,Crivello, Antonino,Zhao, Zhongliang,et al. Indoor Positioning Based on Fingerprint-Image and Deep Learning[J]. IEEE ACCESS,2018,6:74699-74712.
APA Zhao, Fang,Crivello, Antonino,Zhao, Zhongliang,Ma, Yan,Shao, Wenhua,&Luo, Haiyong.(2018).Indoor Positioning Based on Fingerprint-Image and Deep Learning.IEEE ACCESS,6,74699-74712.
MLA Zhao, Fang,et al."Indoor Positioning Based on Fingerprint-Image and Deep Learning".IEEE ACCESS 6(2018):74699-74712.

入库方式： OAI收割

来源：计算技术研究所

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