A machine learning approach for localization in cellular environments

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dc.contributor.author Abdallah, Ali A.
dc.contributor.author Saab, Samer S.
dc.contributor.author Kassas, Zaher M.
dc.date.accessioned 2019-08-27T10:22:31Z
dc.date.available 2019-08-27T10:22:31Z
dc.date.copyright 2018 en_US
dc.date.issued 2019-08-27
dc.identifier.isbn 9781538616475 en_US
dc.identifier.uri http://hdl.handle.net/10725/11237
dc.description.abstract A machine learning approach is developed for localization based on received signal strength (RSS) from cellular towers. The proposed approach only assumes knowledge of RSS fingerprints of the environment, and does not require knowledge of the cellular base transceiver station (BTS) locations, nor uses any RSS mathematical model. The proposed localization scheme integrates a weighted K-nearest neighbor (WKNN) and a multilayer neural network. The integration takes advantage of the robust clustering ability of WKNN and implements a neural network that could estimate the position within each cluster. Experimental results are presented to demonstrate the proposed approach in two urban environments and one rural environment, achieving a mean distance localization error of 5.9 m and 5.1 m in the urban environments and 8.7 m in the rural environment. This constitutes an improvement of 41%, 45%, and 16%, respectively, over the WKNN-only algorithm. en_US
dc.description.sponsorship IEEE Aerospace and Electronic Systems Society en_US
dc.description.sponsorship Institute of Navigation en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Navigation (Aeronautics) -- Congresses en_US
dc.subject Navigation (Astronautics) -- Congresses en_US
dc.subject Electronics in navigation -- Congresses en_US
dc.subject Navigation -- Congresses en_US
dc.title A machine learning approach for localization in cellular environments en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SOE en_US
dc.author.idnumber 199690250 en_US
dc.author.department Computer Science And Mathematics en_US
dc.description.embargo N/A en_US
dc.description.physdesc 1569 pages : illustrations en_US
dc.publication.place Piscataway, N.J. en_US
dc.description.bibliographiccitations Includes bibliographical references. en_US
dc.identifier.doi http://dx.doi.org/10.1109/PLANS.2018.8373508 en_US
dc.identifier.ctation Abdallah, A. A., Saab, S. S., & Kassas, Z. M. (2018, April). A machine learning approach for localization in cellular environments. In 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) (pp. 1223-1227). IEEE. en_US
dc.author.email ssaab@lau.edu.lb en_US
dc.conference.date April 23-26, 2018 en_US
dc.conference.pages 1223-1227 en_US
dc.conference.place Monterey, California en_US
dc.conference.subtitle IEEE/ION Position Location and Navigation Symposium (PLANS) : proceedings en_US
dc.conference.title PLANS 2018 en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://ieeexplore.ieee.org/abstract/document/8373508 en_US
dc.orcid.id https://orcid.org/0000-0003-0124-8457 en_US
dc.publication.date 2018 en_US
dc.author.affiliation Lebanese American University en_US

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