TY - GEN
T1 - Indoor localization system based on wearable posture sensors with incomplete observations
AU - Wang, Yuan
AU - Huang, Jian
AU - Wang, Yongji
AU - Tao, Chunjing
AU - Yan, Heping
AU - Ma, Lifang
N1 - Publisher Copyright:
© 2014 Swinburne University of Technology, Australia.
PY - 2015/1/23
Y1 - 2015/1/23
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84923484202
U2 - 10.1109/ICMIC.2014.7020780
DO - 10.1109/ICMIC.2014.7020780
M3 - 会议稿件
AN - SCOPUS:84923484202
T3 - Proceedings of 2014 International Conference on Modelling, Identification and Control, ICMIC 2014
SP - 355
EP - 360
BT - Proceedings of 2014 International Conference on Modelling, Identification and Control, ICMIC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Modelling, Identification and Control, ICMIC 2014
Y2 - 3 December 2014 through 5 December 2014
ER -