A Spatial-Temporal Positioning Algorithm Using Residual Network and LSTM

doi:10.1109/TIM.2020.2998645

	A Spatial-Temporal Positioning Algorithm Using Residual Network and LSTM
	Wang, Rongrong 1; Luo, Haiyong 2; Wang, Qu 3; Li, Zhaohui 1; Zhao, Fang 1; Huang, Jingyu 1
	2020-11-01
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
卷号	69 期号:11 页码:9251-9261
摘要	With the ever-increasing demand for location-based services in the indoor environments, Wi-Fi-based positioning technology has attracted much attention in decades of years because of its ubiquitous deployment and low cost. There is the fact that Wi-Fi signal not only changes with the distance away from the target, but also changes with time. To improve positioning accuracy and robustness, we consider both the spatial relation and temporal sequential relation simultaneously, and propose a spatial-temporal positioning algorithm that combines residual network and long short-term memory (LSTM) network. In this algorithm, to avoid the degradation problem, we adopt the residual-based network to extract the spatial features of the Wi-Fi signal at the same time slice. Furthermore, the LSTM is used to extract temporal features of the Wi-Fi signal among successive time slices. Finally, a fully connected layer is used to obtain the final location estimation. Extensive experiments on the IPIN2016 data sets demonstrate that our proposed algorithm can obtain 4.93-, 5.40-, 3.20-, and 4.98-m average positioning error on the UAH, CAR, UJIUB, and UJITI subdata set, respectively. The experimental results show that our proposed algorithm outperforms other state-of-the-art positioning algorithms with better accuracy and robustness.
关键词	Wireless fidelity Feature extraction Fingerprint recognition Residual neural networks Deep learning Robustness Degradation Convolutional neural network (CNN) indoor positioning long short-term memory (LSTM) residual network spatial-temporal
DOI	10.1109/TIM.2020.2998645
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2016YFB0502000] ; Action Plan Project of the Beijing University of Posts and Telecommunications - Fundamental Research Funds for the Central Universities[2019XD-A06] ; Special Project for Youth Research and Innovation, Beijing University of Posts and Telecommunications ; Fundamental Research Funds for the Central Universities[2019PTB-011] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[61761038] ; Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Key Research and Development Project from Hebei Province[19210404D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000577673200059
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：32[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/2XEOYT63/15755
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
作者单位	1.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Wang, Rongrong,Luo, Haiyong,Wang, Qu,et al. A Spatial-Temporal Positioning Algorithm Using Residual Network and LSTM[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2020,69(11):9251-9261.
APA	Wang, Rongrong,Luo, Haiyong,Wang, Qu,Li, Zhaohui,Zhao, Fang,&Huang, Jingyu.(2020).A Spatial-Temporal Positioning Algorithm Using Residual Network and LSTM.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,69(11),9251-9261.
MLA	Wang, Rongrong,et al."A Spatial-Temporal Positioning Algorithm Using Residual Network and LSTM".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 69.11(2020):9251-9261.