MOC: Wi-Fi FTM With Motion Observation Chain for Pervasive Indoor Positioning
文献类型：期刊论文
| 作者 | Shao, Wenhua1; Luo, Haiyong2,3; Zhao, Fang1; Hong, Yunhan1; Li, Yaqi1; Zhang, Chen1; Sun, Bingzheng1; Crivello, Antonino4 |
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2024-07-02 |
| 页码 | 16 |
| 关键词 | Distance measurement Accuracy Position measurement Wireless fidelity Motion measurement Protocols Indoor environment Factor graph indoor localization motion model ranging model time-of-flight |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2024.3413342 |
| 英文摘要 | The IEEE 802.11-2016 standard enables devices to gather precise ranging information through the time-of-flight evaluation, facilitating the development of accurate indoor location-based services. Researchers have indicated that the protocol's most effective performance is in scenarios with direct line-of-sight, despite providing meter-level ranging accuracy. In real indoor environments, the accuracy diminishes considerably due to random errors caused by interference such as multipath effects and non-line-of-sight signal propagation. Therefore, it is essential to accurately evaluate the reliability of each ranging measurement and effectively leverage neighboring high-quality observations to improve positioning accuracy. This study presents a novel optimization algorithm that relies on the motion observation series by incorporating adjacent ranging observations and a priori motion knowledge into a factor graph model, resulting in a unified optimization objective. Consequently, our system can dynamically estimate the confidence of fine time measurements ranging measurements. It optimizes the position estimation of the current user by maximizing the probability of not only the current ranging measurements but also the adjacent historical measurements and a priori motion. Additionally, to enable real-time positioning, a fast-solving procedure employing an adaptive gradient is proposed, capable of providing evaluations in under 10ms. The system has been tested in real indoor environments, showing improved performance compared to existing methods. It achieves meter-level real-time positioning accuracy at 1 sigma without requiring a specific device pose, additional sensor, or expensive site survey. This makes our proposal highly applicable for wide adoption and readiness for the market. |
| 资助项目 | Beijing Natural Science Foundation[4222034] ; Beijing Natural Science Foundation[4232035] ; Beijing Natural Science Foundation[4212024] ; National Key R&D Program of China[2022YFB3904702] ; National Natural Science Foundation of China[62261042] ; National Natural Science Foundation of China[62002026] ; Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation ; Fengtai Rail Transit Frontier Research Joint Fund[L221003] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA28040500] ; Fundamental Research Funds for the Central Universities[2022RC13] ; Yibin City Introduction of High-Level Talent Project[2022YG03] ; Open Project of the Beijing Key Laboratory of Mobile, Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001263416900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39856]  |
| 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, Fang |
| 作者单位 | 1.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100876, Peoples R China 4.CNR, Inst Informat Sci & Technol, I-00186 Rome, Italy |
| 推荐引用方式 GB/T 7714 | Shao, Wenhua,Luo, Haiyong,Zhao, Fang,et al. MOC: Wi-Fi FTM With Motion Observation Chain for Pervasive Indoor Positioning[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2024:16. |
| APA | Shao, Wenhua.,Luo, Haiyong.,Zhao, Fang.,Hong, Yunhan.,Li, Yaqi.,...&Crivello, Antonino.(2024).MOC: Wi-Fi FTM With Motion Observation Chain for Pervasive Indoor Positioning.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,16. |
| MLA | Shao, Wenhua,et al."MOC: Wi-Fi FTM With Motion Observation Chain for Pervasive Indoor Positioning".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2024):16. |
入库方式： OAI收割
来源：计算技术研究所
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