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Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones

文献类型：期刊论文

作者Yu, Chen1; Luo, Haiyong2; Fang, Zhao1; Qu, Wang1; Shao, Wenhua1
刊名INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
出版日期2020-05-01
卷号17期号:3页码:14
关键词Pedestrian navigation error model system inertial sensors integration magnetic field heading estimation
ISSN号1729-8814
DOI10.1177/1729881420930934
英文摘要Pedestrian navigation with daily smart devices has become a vital issue over the past few years and the accurate heading estimation plays an essential role in it. Compared to the pedestrian dead reckoning (PDR) based solutions, this article constructs a scalable error model based on the inertial navigation system and proposes an adaptive heading estimation algorithm with a novel method of relative static magnetic field detection. To mitigate the impact of magnetic fluctuation, the proposed algorithm applies a two-way Kalman filter process. Firstly, it achieves the historical states with the optimal smoothing algorithm. Secondly, it adjusts the noise parameters adaptively to reestimate current attitudes. Different from the pedestrian dead reckoning-based solution, the error model system in this article contains more state information, which means it is more sensitive and scalable. Moreover, several experiments were conducted, and the experimental results demonstrate that the proposed heading estimation algorithm obtains better performance than previous approaches and our system outperforms the PDR system in terms of flexibility and accuracy.
资助项目National Key Research and Development Program[2019YFC1511400] ; 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 Regio[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向Robotics
语种英语
WOS记录号WOS:000544697500001
出版者SAGE PUBLICATIONS INC
源URL[http://119.78.100.204/handle/2XEOYT63/15085]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yu, Chen
作者单位1.Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Chen,Luo, Haiyong,Fang, Zhao,et al. Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2020,17(3):14.
APA Yu, Chen,Luo, Haiyong,Fang, Zhao,Qu, Wang,&Shao, Wenhua.(2020).Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,17(3),14.
MLA Yu, Chen,et al."Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 17.3(2020):14.

入库方式： OAI收割

来源：计算技术研究所

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