Title: Wearable sensor-based indoor localisation system considering incomplete observations

Authors: Yuan Wang; Jian Huang; Yongji Wang

Addresses: School of Automation, Huazhong University of Science and Technology, Wuhan, China ' School of Automation, Huazhong University of Science and Technology, Wuhan, China ' School of Automation, Huazhong University of Science and Technology, Wuhan, China

Abstract: Radio frequency identification (RFID)-based indoor localisation has attracted much interest from robotic researchers. To deal with the deficiency that plentiful tags are required in a conventional RFID-based localisation system, this paper presents an indoor localisation method by fusing measurements from wearable posture sensors and the absolute position information from scattered RFID tags. Using the posture sensors, the relative indoor localisation data are acquired by summing up the vectors composed of step length and heading direction. Considering the performance of relative localisation is 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. Since 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 localisation accuracy is enhanced through the sensor fusion.

Keywords: radio frequency identification; RFID; wearable sensors; indoor localisation; Kalman filtering; incomplete observations; gait identification; posture sensors; step length; heading direction; sensor fusion.

DOI: 10.1504/IJMIC.2015.072980

International Journal of Modelling, Identification and Control, 2015 Vol.24 No.4, pp.291 - 299

Received: 13 Mar 2015
Accepted: 13 Mar 2015
Published online: 11 Nov 2015 *

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