DC Field | Value | Language |
---|---|---|
dc.contributor.author | Keunho Yun | - |
dc.contributor.author | Sukwon Choi | - |
dc.contributor.author | Kim, D | - |
dc.date.accessioned | 2016-04-01T01:50:32Z | - |
dc.date.available | 2016-04-01T01:50:32Z | - |
dc.date.created | 2009-08-19 | - |
dc.date.issued | 2006-08 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | 2006-OAK-0000006222 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23824 | - |
dc.description.abstract | A dangerous workplace like the iron production company needs a durable monitoring of workers to protect them from an critical accident. This paper concerns about a robust and accurate location tracking method using ubiquitous RFID wireless network. The sensed RSSI signals obtained from the RFID readers are very unstable in the complicated and propagation-hazard workplace like the iron production company. So, the existing particle filter can not provide a satisfactory location tracking performance. To overcome this limitation, we propose a double layered particle filter, where the lower layer classifies the block in which the tag is contained by the SVM classifier and the upper layer estimates the accurate location of tag owner by the particle filter within the classified block. This layered structure improves the location estimation and tracking performance because the evidence about the location from the lower layer makes a effective restrict on the range of possible locations of the upper layer. We implement the proposed location estimation and tracking system using the ubiquitous RFID wireless network in a noisy and complicated workplace (100m x 50m) where which 49 RFID readers and 9 gateways are located in the fixed locations and the maximally 100 workers owning active RFID tags are moving around the workplace. Many extensive experiments show that the proposed location estimation and tracking system is working well in a real-time and the position error is about 2m at maximum. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.isPartOf | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.title | A ROBUST LOCATION TRACKING USING UBIQUITOUS RFID WIRELESS NETWORK | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1007/11833529_12 | - |
dc.author.google | Yun, K | - |
dc.author.google | Choi, S | - |
dc.author.google | Kim, D | - |
dc.relation.volume | 4159 | - |
dc.relation.issue | 1 | - |
dc.relation.startpage | 113 | - |
dc.relation.lastpage | 124 | - |
dc.contributor.id | 10054411 | - |
dc.relation.journal | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Conference Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | LECTURE NOTES IN COMPUTER SCIENCE, v.4159, no.1, pp.113 - 124 | - |
dc.identifier.wosid | 000240542600012 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 124 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 113 | - |
dc.citation.title | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.citation.volume | 4159 | - |
dc.contributor.affiliatedAuthor | Kim, D | - |
dc.identifier.scopusid | 2-s2.0-33750080715 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 1 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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