Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks

Sithamparanathan, K; Reisenfeld, S; Aysal, TC; Lowe, DB; Piesiewicz, R

Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks

Sithamparanathan, K Reisenfeld, S Aysal, TC Lowe, DB Piesiewicz, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 69th IEEE Vehicular Technology Conference, 2009, pp. 1 - 5
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Sithamparanathan, K	en_US
dc.contributor.author	Reisenfeld, S	en_US
dc.contributor.author	Aysal, TC	en_US
dc.contributor.author	Lowe, DB	en_US
dc.contributor.author	Piesiewicz, R	en_US
dc.contributor.editor	Agust, R	en_US
dc.contributor.editor	Ripoll, JL	en_US
dc.date	2009-04-26	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	69th IEEE Vehicular Technology Conference, 2009, pp. 1 - 5	en_US
dc.identifier.isbn	978-1-4244-2517-4	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.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	69th IEEE Vehicular Technology Conference	en_US
dc.relation.ispartof	IEEE Vehicular Technology Conference	en_US
dc.relation.isbasedon	10.1109/VETECS.2009.5073417	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Barcelona, Spain	en_US
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Barcelona, Spain	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2009-04-26	en_US

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.

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http://hdl.handle.net/10453/11206