김선우
2016-08-30T02:24:14Z
2016-08-30T02:24:14Z
2015-03
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, Page. 1-11
1550-1329
1550-1477
http://dsn.sagepub.com/content/11/8/674635.full
http://hdl.handle.net/20.500.11754/22847
This paper investigates the indoor position tracking problem under the variation of received signal strength (RSS) characteristic from the changes of device statuses and environmental factors. A novel indoor position tracking algorithmis introduced to provide reliable position estimates by integrating motion sensor-based positioning (i.e., dead-reckoning) and RSS-based fingerprinting positioning with Kalman filter. In the presence of the RSS variation, RSS-based fingerprinting positioning provides unreliable results due to different characteristics of RSS measurements in the offline and online phases, and the tracking performance is degraded. To mitigate the effect of the RSS variation, a recursive least square estimation-based self-calibration algorithm is proposed that estimates the RSS variation parameters and provides the mapping between the offline and online RSS measurements. By combining the Kalman filter-based tracking algorithm with the self-calibration, the proposed algorithm can achieve higher tracking accuracy even in severe RSS variation conditions. Through extensive computer simulations, we have shown that the proposed algorithm outperforms other position tracking algorithms without self-calibration.
en
HINDAWI PUBLISHING CORPORATION
VARIANCE PROBLEM
WI-FI
SYSTEMS
NAVIGATION
NETWORKS
Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation
Article
10.1155/2015/674635
1-11
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Lee, Sangwoo
Cho, Bongkwan
Koo, Bonhyun
Ryu, Sanghwan
Choi, Jaehoon
Kim, Sunwoo
2015008330
S
COLLEGE OF ENGINEERING[S]
DEPARTMENT OF ELECTRONIC ENGINEERING
remero