Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning

doi:10.1109/LCOMM.2021.3090300

	Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning
	Shao, Wenhua 1,2; Zhao, Fang 1; Luo, Haiyong 3,4; Tian, Hui 2; Li, Jiaxin 1; Crivello, Antonino 5
	2021-09-01
发表期刊	IEEE COMMUNICATIONS LETTERS
ISSN	1089-7798
卷号	25 期号:9 页码:3144-3148
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.
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
收录类别	SCI
语种	英语
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
WOS类目	Telecommunications
WOS记录号	WOS:000694697800079
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[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/2XEOYT63/17174
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 Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, 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 100876, Peoples R China 4.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100876, Peoples R China 5.Natl Res Council CNR, Inst Informat Sci & Technol, I-56124 Pisa, Italy
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.