Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning
文献类型：期刊论文
| 作者 | Shao, Wenhua2,3; Zhao, Fang2; Luo, Haiyong4,5; Tian, Hui3; Li, Jiaxin2; Crivello, Antonino1 |
| 刊名 | IEEE COMMUNICATIONS LETTERS
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 25期号:9页码:3144-3148 |
| ISSN号 | 1089-7798 |
| 关键词 | Particle filters Neural networks Reinforcement learning Mathematical model Particle measurements Estimation Atmospheric measurements Indoor location tracking particle filter pedestrian dead reckoning reinforcement learning smartphone-based navigation |
| DOI | 10.1109/LCOMM.2021.3090300 |
| 英文摘要 | Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distribution. Finally, the selection of eligible directions is performed applying external constraints mainly imposed from the indoor map. However, the choice of a constant parameter decreases the positioning performances because the variance of nearby context, including topography, ferromagnetic materials, and particle distribution, is not represented. Therefore, we propose the particle filter reinforcement able to adaptively learn and adjust the variance of the direction observing the context in real-time. Experiments in real-world scenarios show that the proposed method improves the positioning accuracy by more than 20% at the 80% probability compared with state-of-the-art methods. |
| 资助项目 | Joint Research Fund for Beijing Natural Science Foundation ; Haidian Original Innovation[L192004] ; National Natural Science Foundation of China[61872046] ; Beijing Natural Science Foundation[4212024] ; Action Plan Project of the Beijing University of Posts and Telecommunications ; Fundamental Research Funds for the Central Universities[2019XD-A06] ; Hebei Province[19210404D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Regio[2019GG328] |
| WOS研究方向 | Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000694697800079 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17173]  |
| 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.Natl Res Council CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy 2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100876, Peoples R China 5.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100876, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shao, Wenhua,Zhao, Fang,Luo, Haiyong,et al. Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning[J]. IEEE COMMUNICATIONS LETTERS,2021,25(9):3144-3148. |
| APA | Shao, Wenhua,Zhao, Fang,Luo, Haiyong,Tian, Hui,Li, Jiaxin,&Crivello, Antonino.(2021).Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning.IEEE COMMUNICATIONS LETTERS,25(9),3144-3148. |
| MLA | Shao, Wenhua,et al."Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning".IEEE COMMUNICATIONS LETTERS 25.9(2021):3144-3148. |
入库方式： OAI收割
来源：计算技术研究所
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