TY - GEN
T1 - A real-time indoor tracking system in smartphones
AU - Carrera, Jose Luis
AU - Li, Zan
AU - Zhao, Zhongliang
AU - Braun, Torsten
AU - Neto, Augusto
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/11/13
Y1 - 2016/11/13
N2 - The rapid growth area of ubiquitous applications and location-based services has made indoor localization an interesting topic for research. Some indoor localization solutions for smartphones exploit radio information and Inertial Measurement Units (IMUs), which are embedded in most of the modern smartphones. In this work, we propose to fuse WiFi Receiving Signal Strength Indicator (RSSI) readings, IMUs, and floor plan information in an enhanced particle filter to achieve high accuracy and stable performance in the tracking process. We provide an efficient double resampling method to mitigate errors caused by off-the-shelf IMUs and WiFi sensors embedded in commodity smartphones. The algorithms are designed in a terminal-based system, which consists of commercial smartphones and WiFi access points. We evaluate our system in two complex environments along moving paths. Experiment results show that our tracking method can achieve the average tracking error of 1.01 meters and 90% accuracy of 1.7 meters.
AB - The rapid growth area of ubiquitous applications and location-based services has made indoor localization an interesting topic for research. Some indoor localization solutions for smartphones exploit radio information and Inertial Measurement Units (IMUs), which are embedded in most of the modern smartphones. In this work, we propose to fuse WiFi Receiving Signal Strength Indicator (RSSI) readings, IMUs, and floor plan information in an enhanced particle filter to achieve high accuracy and stable performance in the tracking process. We provide an efficient double resampling method to mitigate errors caused by off-the-shelf IMUs and WiFi sensors embedded in commodity smartphones. The algorithms are designed in a terminal-based system, which consists of commercial smartphones and WiFi access points. We evaluate our system in two complex environments along moving paths. Experiment results show that our tracking method can achieve the average tracking error of 1.01 meters and 90% accuracy of 1.7 meters.
KW - Inertial Measurement Units (IMU)
KW - Particle Filter
KW - Received Signal Strength Indicator (RSSI)
KW - WiFi
UR - https://www.scopus.com/pages/publications/85007023214
U2 - 10.1145/2988287.2989142
DO - 10.1145/2988287.2989142
M3 - 会议稿件
AN - SCOPUS:85007023214
T3 - MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
SP - 292
EP - 301
BT - MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
PB - Association for Computing Machinery, Inc
T2 - 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2016
Y2 - 13 November 2016 through 17 November 2016
ER -