Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation

Title
Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation
Authors
김선우
Keywords
VARIANCE PROBLEM; WI-FI; SYSTEMS; NAVIGATION; NETWORKS
Issue Date
2015-03
Publisher
HINDAWI PUBLISHING CORPORATION
Citation
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, Page. 1-11
Abstract
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.
URI
http://dsn.sagepub.com/content/11/8/674635.fullhttp://hdl.handle.net/20.500.11754/22847
ISSN
1550-1329; 1550-1477
DOI
http://dx.doi.org/10.1155/2015/674635
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > DEPARTMENT OF ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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