Enhancing positioning accuracy through direct position estimators based on hybrid RSS data fusion

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. On the one hand, indirect RSS-based estimation schemes are investigated; these schemes are based on two steps of estimation: estimation of ranges from RSS and then estimation of position using weighted least square approximation. We show that the performances of this type of schemes depend on the used estimator in the first step.We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. On the other hand, a new direct RSS-based estimation scheme of position is proposed; Monte Carlo simulations show that the new estimator performs better than indirect estimators and can be reliable in future hybrid localization systems.

Mots clés: Localization; RSS; Indirect vs Direct Location Estimation; Weighted least square; Hybrid Data Fusion; 4G networks; ranging; Path Loss; Log Normal Shadowing

Domaines: Réseaux et télécommunications [cs.NI]

Mohamed Laaraiedh, Stéphane Avrillon, Bernard Uguen. Enhancing positioning accuracy through direct position estimators based on hybrid RSS data fusion. Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th, Apr 2009, Barcelona, Spain. pp.1 - 5. ⟨hal-00379638⟩