Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks

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dc.contributor.author Sithamparanathan, K
dc.contributor.author Reisenfeld, S
dc.contributor.author Aysal, TC
dc.contributor.author Lowe, DB
dc.contributor.author Piesiewicz, R
dc.contributor.editor Agust, R
dc.contributor.editor Ripoll, JL
dc.date.accessioned 2010-05-28T10:02:38Z
dc.date.issued 2009-01
dc.identifier.citation 69th IEEE Vehicular Technology Conference, 2009, pp. 1 - 5
dc.identifier.isbn 978-1-4244-2517-4
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/11206
dc.description.abstract In this paper we consider cooperative localization and tracking of primary users (PU) in a cognitive radio network using Bayesian techniques. We use particle filtering methods to track the location of a PU in the network using cooperative localization techniques and present some results for noisy measurements. The cognitive radio (CR) nodes estimate the information related to the geographical position of the PU based on existing location identification and localization techniques and forward the noisy information to a cognitive radio base station (CRB), which then fuses the information to estimate the position of the PU in the network in order to perform a radio scene analysis. We propose a particle filtering approach that is suitable for tracking Gaussian and non-Gaussian noisy signals at the CRB to estimate the position of a PU, two importance-functions relative to the particle filtering algorithm are also presented. Simulations are performed on the proposed tracking algorithm and the results are presented in terms of the mean squared error of the positional estimates.
dc.publisher IEEE
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1109/VETECS.2009.5073417
dc.title Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks
dc.type Conference Proceeding
dc.parent 69th IEEE Vehicular Technology Conference
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 5 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference IEEE Vehicular Technology Conference
dc.for 0902 Automotive Engineering
dc.personcode 880567
dc.personcode 930311
dc.percentage 100 en_US
dc.classification.name Automotive Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE Vehicular Technology Conference en_US
dc.date.activity 20090426 en_US
dc.date.activity 2009-04-26
dc.location.activity Barcelona, Spain en_US
dc.description.keywords Bayesian tracking , Gaussian noise , base station , cognitive radio networks , cooperative localization techniques , location identification , mean squared error methods , non-Gaussian noise , particle filtering methods , primary user tracking en_US
dc.description.keywords Bayesian tracking , Gaussian noise , base station , cognitive radio networks , cooperative localization techniques , location identification , mean squared error methods , non-Gaussian noise , particle filtering methods , primary user tracking
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
pubs.organisational-group /University of Technology Sydney/Strength - Realtime Information Networks
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)
utslib.collection.history School of Computing and Communications (ID: 335)


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