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ABSTRACT
The pedestrian dead reckoning (PDR) is a popular technique for recognizing a pedestrian’s trajectory. However, its inherent disadvantage of susceptibility to error accumulation limits its performance as the travelled distance is increased. Various techniques are combined with PDR to mitigate the error accumulation by regular position resetting. The radio frequency (RF) Tomography is an emerging technology for localization by sensing the received signal strength (RSS) fluctuation on a line-of-sight wireless link (LOSL). Conventional RF Tomography based schemes need specific large-scale networks and face with the problem of harsh environments and high hardware costs. Thus in this paper, we proposed the flexible and practical GF operators for PDR resetting work, in which we introduce the displacement estimation by PDR technique and the LOSL triggering sequence to build a PDR geometrical formulation (PDR-GF) model, and then this model is optimized by Particle Swarm Optimization (PSO) algorithm for crossing point (CP) estimation on the LOSLs. Finally, the estimated CP is in turn used for regular position resetting. Practical experiments are performed to evaluate the proposed positioning scheme. According to the results, the proposed scheme can achieve a finer accuracy of less than 1 m, outperforming the existing RSS-PDR positioning scheme.
Acknowledgments
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the ministry of Education (NRF-2016R1D1A1B03932980). This work was supported in part by the National Natural Science Foundation of China (No. 61703185) and the Fundamental Research Funds for the Central Universities From the ministry of Education of PRC (No. 1252050205171780).
Disclosure statement
No potential conflict of interest was reported by the authors.
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Notes on contributors
Biao Zhou
Biao Zhou received the B.S degree from Qingdao University of Science and Technology, China, in 2011 and earned his doctorate at Kwangwoon University, Seoul, Korea in 2016. He is now working as an Assistant Professor in the School of Internet of Things Engineering at Jiangnan University, Wuxi, China. His research interests include passive tracking and localization systems in wireless sensor networks, including positioning model building and optimization.
Changqiang Jing
Changqiang Jing received the B.S degree from Qingdao University of Science & Technology China, in 2008, and Ph.D from Kwangwoon University, Seoul, Korea and 2015. In 2015, he joined the School of Informatics, Linyi University, China, as a faculty member. His research interests include ultra-wideband system, embedded system, and high accuracy positioning system.
Chao Sun
Chao Sun received the B.S degree from Qingdao University of Science and Technology, China, in 2016. He is working toward the Master and Ph.D. Degrees in the Advanced Wireless System and Technology Lab, Kwangwoon University, Seoul, Korea. His research interests include ultra-wideband system, and high accuracy positioning system, passive tracking system.
Youngok Kim
Youngok Kim received the B.S. Degree in mechanical engineering from Yonsei University, Seoul, Korea in 1999, and the M.S. and Ph.D. Degrees in electrical and computer engineering from the University of Texas at Austin, Austin, in 2002 and 2006, respectively. From 2006 to 2008, he was a senior researcher at Infra Laboratory of Korea Telecom (KT), Seoul, Korea. From March 2008, He is now working as an a Professor at the Department of Electronic Engineering of Kwangwoon University, Seoul, Korea. His research interests include ultra-wide band wireless communication systems, OFDM-based systems, precise ranging and positioning systems.