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Statistical path loss parameter estimation and positioning using RSS measurements

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Details

Original languageEnglish
Title of host publicationUbiquitous Positioning, Indoor Navigation and Location-Based Services, UPINLBS, 3-4 October 2012, Helsinki
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-4673-1909-6
ISBN (Print)978-1-4673-1908-9
DOIs
Publication statusPublished - 2012
Publication typeA4 Article in a conference publication

Publication series

NameUbiquitous Positioning, Indoor Navigation and Location-Based Services

Abstract

An efficient Bayesian method for off-line estimation of the position and the path loss model parameters of a base station is presented. Two versions of three different on-line positioning methods are tested using real data collected from a cellular network. The tests confirm the superiority of the methods that use the estimated path loss parameter distributions compared to the conventional methods that only use point estimates for the path loss parameters. Taking the uncertainties into account is computationally demanding, but the Gauss–Newton optimization methods is shown to provide a good approximation with computational load that is reasonable for many real-time solutions.

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