Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최재훈 | - |
dc.date.accessioned | 2017-04-27T00:43:21Z | - |
dc.date.available | 2017-04-27T00:43:21Z | - |
dc.date.issued | 2015-08 | - |
dc.identifier.citation | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 논문 번호: 674635, Page. 1-10 | en_US |
dc.identifier.issn | 1550-1329 | - |
dc.identifier.issn | 1550-1477 | - |
dc.identifier.uri | http://journals.sagepub.com/doi/full/10.1155/2015/674635 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/27004 | - |
dc.description.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. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (nos. 2010-0017934 and NRF-2013R1A1A2062728) and by a grant from R&D Program of the Korea Railroad Research Institute, Republic of Korea. | en_US |
dc.language.iso | en | en_US |
dc.publisher | HINDAWI PUBLISHING CORPORATION | en_US |
dc.subject | VARIANCE PROBLEM | en_US |
dc.subject | WI-FI | en_US |
dc.subject | SYSTEMS | en_US |
dc.subject | NAVIGATION | en_US |
dc.subject | NETWORKS | en_US |
dc.title | Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1155/2015/674635 | - |
dc.relation.page | 1-10 | - |
dc.relation.journal | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | - |
dc.contributor.googleauthor | Lee, Sangwoo | - |
dc.contributor.googleauthor | Cho, Bongkwan | - |
dc.contributor.googleauthor | Koo, Bonhyun | - |
dc.contributor.googleauthor | Ryu, Sanghwan | - |
dc.contributor.googleauthor | Choi, Jaehoon | - |
dc.contributor.googleauthor | Kim, Sunwoo | - |
dc.relation.code | 2015008330 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | choijh | - |
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