Indoor localization system based on wearable posture sensors with incomplete observations

Radio Frequency Identification (RFID) based indoor localization becomes a hotspot in the robotic research field recently. To overcome the shortcoming that plentiful tags are required in a normal RFID based localization system, this paper presents an indoor localization method by fusing measurements from wearable posture sensors and the absolute position information from scattered RFID tags. From the posture sensors, we can obtain the relative indoor localization data by summing up the vectors composed of step length and heading direction. Since this relative localization is highly affected by the cumulative error, the absolute positions of RFID tags are used as corrections if they are found within a read-range to the user. Because the RFID tags are sparsely placed in the indoor environment, the corrections can be achieved only at incomplete time instants. Therefore, a revised Kalman filter with incomplete observation is applied to the sensor fusion between the posture sensors and RFID tags. Experimental results show that the cumulative error of the system can be significantly reduced and the localization accuracy is enhanced through the sensor fusion.

[1]  Yunhao Liu,et al.  ANDMARC: Indoor Location Sensing Using Active RFID , 2003, PerCom.

[2]  Hari Balakrishnan,et al.  Tracking moving devices with the cricket location system , 2004, MobiSys '04.

[3]  Theodore S. Rappaport,et al.  An overview of the challenges and progress in meeting the E-911 requirement for location service , 1998, IEEE Commun. Mag..

[4]  Ana M. Bernardos,et al.  A fusion method based on bluetooth and WLAN technologies for indoor location , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[5]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[6]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[7]  José A. Gallud,et al.  Using active and passive RFID technology to support indoor location-aware systems , 2008, IEEE Transactions on Consumer Electronics.

[8]  Gomes Goncalo,et al.  Indoor Location System Using ZigBee Technology , 2009, 2009 Third International Conference on Sensor Technologies and Applications.

[9]  K. Kaemarungsi,et al.  Distribution of WLAN received signal strength indication for indoor location determination , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[10]  Jian Huang,et al.  An Electronic Travel Aid based on multi-sensor fusion using extended Kalman filter , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[11]  Alberto Elfes,et al.  Sonar-based real-world mapping and navigation , 1987, IEEE J. Robotics Autom..

[12]  José Luis Rojo-Álvarez,et al.  Time-Space Sampling and Mobile Device Calibration for WiFi Indoor Location Systems , 2011, IEEE Transactions on Mobile Computing.

[13]  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).

[14]  Ali Saman Tosun,et al.  Low cost indoor location management system using infrared leds and Wii Remote Controller , 2009, 2009 IEEE 28th International Performance Computing and Communications Conference.