Wearable sensor-based indoor localisation system considering incomplete observations
by Yuan Wang; Jian Huang; Yongji Wang
International Journal of Modelling, Identification and Control (IJMIC), Vol. 24, No. 4, 2015

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.

Online publication date: Wed, 11-Nov-2015

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