WiBEST: A hybrid personal indoor positioning system

This paper introduces WiBEST, Wireless Body and Environmental Sensor Tracking platform, for personal indoor positioning. WiBEST is built with portable on-body sensor nodes and assisted sensor nodes deployed in the targeted indoor area. It takes a hybrid approach with pedestrian dead reckoning and radio-based localization and explore the their cooperative efforts. Real-time inertial measurements are combined with RSSI-based information, and then processed with an Extended Kalman Filter to be weighted in the location estimation according to their reliability. WiBEST also incorporates with an adaptive Step Length Algorithm to reduce the deviation of the measurements.The experimentation results show that WiBEST can improve the accuracy of the positioning by 66.3% compared to pure inertial solution. With the popularity of wearable devices with inertial sensors and wireless communication chips, we believe that this approach is very promising for personal indoor positioning services.

[1]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Helena Leppäkoski,et al.  Error Analysis of Step Length Estimation in Pedestrian Dead Reckoning , 2002 .

[3]  Henk L. Muller,et al.  Personal position measurement using dead reckoning , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[4]  Gérard Lachapelle,et al.  Indoor Positioning System Using Accelerometry and High Accuracy Heading Sensors , 2003 .

[5]  Wolfgang Effelsberg,et al.  COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses , 2006, WINTECH.

[6]  H. Haas,et al.  Pedestrian Dead Reckoning : A Basis for Personal Positioning , 2006 .

[7]  Abdelmoula Bekkali,et al.  RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering , 2007, Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007).

[8]  J.W. Kim,et al.  Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors , 2007, 2007 IEEE Sensors Applications Symposium.

[9]  Ignas Niemegeers,et al.  A survey of indoor positioning systems for wireless personal networks , 2009, IEEE Communications Surveys & Tutorials.

[10]  Mehul Motani,et al.  SparseTrack: Enhancing Indoor Pedestrian Tracking with Sparse Infrastructure Support , 2010, 2010 Proceedings IEEE INFOCOM.