title: Dynamic chanel model LMS updating for RSS-based localization
creator: Tarrío Alonso, Paula
creator: Bernardos Barbolla, Ana M.
creator: Xian, Wan
creator: Casar Corredera, José Ramón
subject: Telecommunications
description: Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes.
publisher: E.T.S.I. Telecomunicación (UPM)
rights: https://creativecommons.org/licenses/by-nc-nd/3.0/es/
date: 2011
type: info:eu-repo/semantics/conferenceObject
type: Presentation at Congress or Conference
source: Proceedings of he 6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011 | 6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011 | 23/05/2011 - 25/05/2011 | Wroclaw, Polonia
type: PeerReviewed
format: application/pdf
language: eng
relation: http://hais.pwr.wroc.pl/
rights: info:eu-repo/semantics/openAccess
identifier: https://oa.upm.es/11637/