TY - GEN
T1 - The Improved Fingerprint-Based Indoor Localization with RFID/PDR/MM Technologies
AU - Wu, Jie
AU - Zhu, Minghua
AU - Xiao, Bo
AU - Qiu, Yunzhou
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The fingerprint based indoor localization is becoming a dominant solution for its high applicability in complex indoor environment. However, the extensive site survey efforts on manpower and time have become a major bottleneck. Based on the crowdsourcing method, the paper puts forward a novel indoor localization with the fusion of RFID (radio frequency identification devices), PDR (pedestrian dead reckoning)and MM (magnetic matching)technologies. First, a zero-effort fingerprint automated construction and site survey update scheme is proposed with the dual-frequency RFID. Second, in order to solve the problem that step length would vary from person to person which results into positioning bias, the RSS technology and floor map is introduced to aid step length estimation. Third, the particle filter is adopted to fuse the advantages of three different technologies to conduct the accurate indoor positioning. In the experiment, the obtained fingerprint database is demonstrated to possess a comparable accuracy with the human-annotated database. Also, the experiment results show that the proposed method achieves the comparable positioning accuracy and the positioning accuracy is 40% higher than the PDR system or RFID positioning system alone.
AB - The fingerprint based indoor localization is becoming a dominant solution for its high applicability in complex indoor environment. However, the extensive site survey efforts on manpower and time have become a major bottleneck. Based on the crowdsourcing method, the paper puts forward a novel indoor localization with the fusion of RFID (radio frequency identification devices), PDR (pedestrian dead reckoning)and MM (magnetic matching)technologies. First, a zero-effort fingerprint automated construction and site survey update scheme is proposed with the dual-frequency RFID. Second, in order to solve the problem that step length would vary from person to person which results into positioning bias, the RSS technology and floor map is introduced to aid step length estimation. Third, the particle filter is adopted to fuse the advantages of three different technologies to conduct the accurate indoor positioning. In the experiment, the obtained fingerprint database is demonstrated to possess a comparable accuracy with the human-annotated database. Also, the experiment results show that the proposed method achieves the comparable positioning accuracy and the positioning accuracy is 40% higher than the PDR system or RFID positioning system alone.
KW - RFID
KW - crowdsourcing
KW - fingerprint database
KW - magnetic matching
KW - particle filter
KW - pedestrian dead reckoning
UR - https://www.scopus.com/pages/publications/85063350267
U2 - 10.1109/PADSW.2018.8644602
DO - 10.1109/PADSW.2018.8644602
M3 - 会议稿件
AN - SCOPUS:85063350267
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 878
EP - 885
BT - Proceedings - 2018 IEEE 24th International Conference on Parallel and Distributed Systems, ICPADS 2018
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018
Y2 - 11 December 2018 through 13 December 2018
ER -