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14-Bayesian Tracking in Cooperative Localization for

14-Bayesian Tracking in Cooperative Localization for -...

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Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks Sithamparanathan Kandeepan, § Sam Reisenfeld, Tuncer Can Aysal, § David Lowe, Radoslaw Piesiewicz, Broadband and Wireless Lab, Create-Net International Research Centre, via alla Cascatta 56D, Trento, Centre for Italy, § Centre for Real-Time Information Networks (CRiN), Faculty of Engineering, University Technology, Sydney 1 Broadway, NSW 2007, Australia Email: [email protected], [email protected], [email protected], [email protected], [email protected], 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 coopera- tive 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. Index Terms —Bayesian phase tracking, particle filter, cooper- ative localization and tracking, cognitive radios I. INTRODUCTION There has been a shift in the trend on wireless commu- nications recently with the introduction of Cognitive Radios (CR) and related concepts [1] - [4]. CR networks are known to share the spectrum in an opportunistic manner with the other co-existing radios by considering them as the primary users (PU). This is considered to be a better way of efficiently utilizing the electro-magnetic radio spectrum. The concept has especially encouraged and motivated the spectrum policy regulators around the world [5], [6] to investigate the tech- nology further. For such technology to be feasible, the CR nodes need to be aware of the surrounding users of the radio spectrum (i.e. the PUs) within the region of interest, hence CR networks require the intelligence of tracking and localizing the PU present within the vicinity of the network to avoid interfering with their transmissions. This motivates us to study precise localization and tracking techniques for CR networks.
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  • Spring '10
  • brown
  • Maximum likelihood, Estimation theory, cognitive radio, cognitive radio networks, CR networks, radio environment map

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