An integrated approach for the estimation of mobile subscriber geolocation

Kikiras, P; Drakoulis, D

dc.contributor.author	Kikiras, P	en
dc.contributor.author	Drakoulis, D	en
dc.date.accessioned	2014-03-01T01:19:54Z
dc.date.available	2014-03-01T01:19:54Z
dc.date.issued	2004	en
dc.identifier.issn	0929-6212	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15755
dc.subject	Data fusion	en
dc.subject	Kalman filtering	en
dc.subject	Mobile geolocation	en
dc.subject	State estimation	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Data fusion	en
dc.subject.other	Mobile geolocation	en
dc.subject.other	Multiple sensor data	en
dc.subject.other	Service providers	en
dc.subject.other	Algorithms	en
dc.subject.other	Information analysis	en
dc.subject.other	Kalman filtering	en
dc.subject.other	Sensor data fusion	en
dc.subject.other	State estimation	en
dc.subject.other	Telecommunication services	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Mobile telecommunication systems	en
dc.title	An integrated approach for the estimation of mobile subscriber geolocation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/B:WIRE.0000049401.97358.c7	en
heal.identifier.secondary	http://dx.doi.org/10.1023/B:WIRE.0000049401.97358.c7	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	The continuous growth of wireless services market, fuels the need for precise location dependent services, leading researchers from academia and industry to reassess existing geolocation methods regarding accuracy and availability of position estimation. The proposed method for mobile subscriber geolocation utilizes key concepts from estimation theory and specifically the Kalman filter algorithm to determine an optimal estimate on the actual system state ( which primarily includes location, velocity) based on the observations acquired by employing network- or terminal-based techniques, which are briefly presented and assessed thereafter. Given the proven limitations of individual techniques, the alternative strategies for fusion of data are outlined, the details of the operation of a fusion scheme based on the Kalman filter are discussed and the impact of the proposed work over conventional methodologies is quantified.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Wireless Personal Communications	en
dc.identifier.doi	10.1023/B:WIRE.0000049401.97358.c7	en
dc.identifier.isi	ISI:000225324600011	en
dc.identifier.volume	30	en
dc.identifier.issue	2-4	en
dc.identifier.spage	217	en
dc.identifier.epage	231	en