Pedestrian Dead Reckoning Based on Walking Pattern Recognition and Online Magnetic Fingerprint Trajectory Calibration
文献类型：期刊论文
| 作者 | Wang, Qu2; Luo, Haiyong3; Xiong, Hao4; Men, Aidong2; Zhao, Fang4; Xia, Ming1; Ou, Changhai5 |
| 刊名 | IEEE INTERNET OF THINGS JOURNAL
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 8期号:3页码:2011-2026 |
| 关键词 | Legged locomotion Trajectory Estimation Fingerprint recognition Dead reckoning Sensors Heading estimation indoor positioning Internet of Things (IoT) online calibration pedestrian dead reckoning (PDR) walking pattern recognition |
| ISSN号 | 2327-4662 |
| DOI | 10.1109/JIOT.2020.3016146 |
| 英文摘要 | With the explosive development of pervasive computing and the Internet of Things (IoT), indoor positioning and navigation have attracted immense attention over recent years. Pedestrian dead reckoning (PDR) is a potential autonomous localization technology that obtains position estimation employing built-in sensors. However, most existing PDR methods assume that the smartphone is held horizontally and points to the walking direction. To solve reckoning errors caused by inconsistency of headings between walking heading and pointing of smartphone, we design an accurate and robust PDR method based on walking patterns, which is identified by multihead convolutional neural networks. In addition to adaptively adjust the threshold of step detection and select the most suitable step length model according to the results of walking pattern recognition, a novel heading estimation approach independent of device orientation is proposed. To mitigate accumulative errors, we proposed an online trajectory calibration method based on forward and backward magnetic fingerprint trajectory matching. We conduct extensive and well-designed experiments in typical scenarios, and the experimental results indicate that the 75th percentile localization accuracy of the three scenarios is 1.06, 1.08, and 1.22 m, respectively, using the commercial smartphone embedded sensor without any dedicated infrastructures or training data. Despite the intricate pedestrian locomotion, the proposed PDR method has great potential in pedestrian positioning. |
| 资助项目 | National Key Research and Development Program[2018YFB0505200] ; 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 ; National Science Foundation of China[61872046] ; National Science Foundation of China[61671264] ; National Science Foundation of China[61671077] ; Joint Research Fund for Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Key Research and Development Project from Hebei Province[19210404D] ; BUPT Excellent Ph.D. Students Foundation[CX2020306] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device ; Fundamental Research Funds for the Central Universities[2019PTB-011] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000612146000058 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16282]  |
| 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; Men, Aidong |
| 作者单位 | 1.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, 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.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 5.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore |
| 推荐引用方式 GB/T 7714 | Wang, Qu,Luo, Haiyong,Xiong, Hao,et al. Pedestrian Dead Reckoning Based on Walking Pattern Recognition and Online Magnetic Fingerprint Trajectory Calibration[J]. IEEE INTERNET OF THINGS JOURNAL,2021,8(3):2011-2026. |
| APA | Wang, Qu.,Luo, Haiyong.,Xiong, Hao.,Men, Aidong.,Zhao, Fang.,...&Ou, Changhai.(2021).Pedestrian Dead Reckoning Based on Walking Pattern Recognition and Online Magnetic Fingerprint Trajectory Calibration.IEEE INTERNET OF THINGS JOURNAL,8(3),2011-2026. |
| MLA | Wang, Qu,et al."Pedestrian Dead Reckoning Based on Walking Pattern Recognition and Online Magnetic Fingerprint Trajectory Calibration".IEEE INTERNET OF THINGS JOURNAL 8.3(2021):2011-2026. |
入库方式： OAI收割
来源：计算技术研究所
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