Vehicle velocity estimation based on RSS measurements

Stamoulakatos, TS; Markopoulos, AS; Anagnostou, ME; Theologou, ME

dc.contributor.author	Stamoulakatos, TS	en
dc.contributor.author	Markopoulos, AS	en
dc.contributor.author	Anagnostou, ME	en
dc.contributor.author	Theologou, ME	en
dc.date.accessioned	2014-03-01T01:27:33Z
dc.date.available	2014-03-01T01:27:33Z
dc.date.issued	2007	en
dc.identifier.issn	0929-6212	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18501
dc.subject	Clustering	en
dc.subject	HIDden Markov Model	en
dc.subject	Location based services	en
dc.subject	Pattern recognition	en
dc.subject	Propagation modeling	en
dc.subject	Traffic information	en
dc.subject	WCDMA	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Code division multiple access	en
dc.subject.other	Data acquisition	en
dc.subject.other	Information retrieval systems	en
dc.subject.other	Information services	en
dc.subject.other	Mobile telecommunication systems	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Telecommunication traffic	en
dc.subject.other	Velocity measurement	en
dc.subject.other	Mobile Terminal (MT) velocity	en
dc.subject.other	Propagation modeling	en
dc.subject.other	Received signal strength (RSS)	en
dc.subject.other	Velocity estimation	en
dc.subject.other	Signal processing	en
dc.title	Vehicle velocity estimation based on RSS measurements	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11277-006-9119-5	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11277-006-9119-5	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	This paper presents a technique which is based on pattern recognition techniques, in order to estimate Mobile Terminal (MT) velocity. The proposed technique applies on received signal strength (RSS) measurements and more precisely on information extracted from Iub air interface, in wIDeband code-division multiple access (WCDMA) systems for transmission control purposes. Pattern recognition is performed by HIDden Markov Model (HMM), which is trained with downlink signal strength measurements for specific areas, employing Clustering LARge Applications (CLARA) like a clustering method. Accurate results from a single probe vehicle show the potential of the method, when applied to large scale of MTs. © Springer Science+Business Media B.V. 2007.	en
heal.publisher	SPRINGER	en
heal.journalName	Wireless Personal Communications	en
dc.identifier.doi	10.1007/s11277-006-9119-5	en
dc.identifier.isi	ISI:000243823300006	en
dc.identifier.volume	40	en
dc.identifier.issue	4	en
dc.identifier.spage	523	en
dc.identifier.epage	538	en