Indoor Positioning Based on Fingerprint-Image and Deep Learning

doi:10.1109/ACCESS.2018.2884193

	Indoor Positioning Based on Fingerprint-Image and Deep Learning
	Shao, Wenhua 1,2; Luo, Haiyong 3,4; Zhao, Fang 1; Ma, Yan 2; Zhao, Zhongliang 5; Crivello, Antonino 6
	2018
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	6 页码:74699-74712
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.
关键词	Indoor positioning indoor localization neural networks fingerprint feature extraction
DOI	10.1109/ACCESS.2018.2884193
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000454390300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：79[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/3485
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Haiyong; Zhao, Zhongliang
作者单位	1.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 2.Beijing Univ Posts & Telecommun, Inst Network Technol, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Univ Bern, Inst Comp Sci, CH-3012 Bern, Switzerland 6.CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy
推荐引用方式 GB/T 7714	Shao, Wenhua,Luo, Haiyong,Zhao, Fang,et al. Indoor Positioning Based on Fingerprint-Image and Deep Learning[J]. IEEE ACCESS,2018,6:74699-74712.
APA	Shao, Wenhua,Luo, Haiyong,Zhao, Fang,Ma, Yan,Zhao, Zhongliang,&Crivello, Antonino.(2018).Indoor Positioning Based on Fingerprint-Image and Deep Learning.IEEE ACCESS,6,74699-74712.
MLA	Shao, Wenhua,et al."Indoor Positioning Based on Fingerprint-Image and Deep Learning".IEEE ACCESS 6(2018):74699-74712.