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Learning Methods for RSSI-based Geolocation: A Comparative Study

Kevin Elgui Pascal Bianchi 1 François Portier 1 Olivier Isson 
1 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : In this paper, we investigate machine learning approaches addressing the problem of geolocation. First, we review some classical learning methods to build a radio map. In particular, these methods are splitted in two categories, which we refer to as likelihood-based methods and fingerprinting methods. Then, we provide a novel geolocation approach in each of these two categories. The first proposed technique relies on a semi-parametric Nadaraya-Watson estimator of the likelihood, followed by a maximum a posteriori (MAP) estimator of the object's position. The second technique consists in learning a proper metric on the dataset, constructed by means of a Gradient boosting regressor: a k-nearest neighbor algorithm is then used to estimate the position. Finally, all the proposed methods are compared on a data set originated from Sigfox network. The experiments show the interest of the proposed methods, both in terms of location estimation performance, and of ability to build radio maps.
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Submitted on : Tuesday, December 3, 2019 - 3:37:13 PM
Last modification on : Saturday, June 25, 2022 - 9:13:27 PM
Long-term archiving on: : Wednesday, March 4, 2020 - 1:26:36 PM


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  • HAL Id : hal-02367908, version 1



Kevin Elgui, Pascal Bianchi, François Portier, Olivier Isson. Learning Methods for RSSI-based Geolocation: A Comparative Study. 27th European Signal Processing Conference (EUSIPCO), Sep 2019, A Coruña, Spain. ⟨hal-02367908⟩



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