A Spatial-Temporal Positioning Algorithm Using Residual Network and LSTM
文献类型：期刊论文
| 作者 | Wang, Rongrong2; Luo, Haiyong1; Wang, Qu3; Li, Zhaohui2; Zhao, Fang2; Huang, Jingyu2 |
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2020-11-01 |
| 卷号 | 69期号:11页码:9251-9261 |
| 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 |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2020.2998645 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000577673200059 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15754]  |
| 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.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, 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. |
入库方式： OAI收割
来源：计算技术研究所
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