Enhancing Positioning Accuracy Through RSS Based Ranging And Weighted Least Square Approximation

Mohamed Laaraiedh (1) , Stéphane Avrillon (1) , Bernard Uguen (1)

1 IETR - Institut d'Électronique et des Technologies du numéRique

In this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. RSS-based estimation schemes of ranges are investigated; three different schemes are studied: Mean, median and mode. Estimation of position is performed using weighted least square approximation. We show that the positioning accuracy depends on the used estimator of ranges from RSS observables. We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. Monte Carlo simulations show that the estimation scheme based on the mode estimator performs better than those based on the median or the mean estimator; and that the use of Weighted Least square approximation enhances the accuracy comparing to typical unweighted least square approximation.

Mots clés: Localization; RSS; Location Estimation; Weighted least square; ranging; Path Loss; Log Normal Shadowing

Domaines: Autre

Mohamed Laaraiedh, Stéphane Avrillon, Bernard Uguen. Enhancing Positioning Accuracy Through RSS Based Ranging And Weighted Least Square Approximation. POCA 2009, May 2009, Antwerpen, Belgium. pp.31-35. ⟨hal-00389017⟩