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Communication Dans Un Congrès Année : 2019

Learning Methods for RSSI-based Geolocation: A Comparative Study

Kevin Elgui
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François Portier
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Olivier Isson
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Résumé

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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Dates et versions

hal-02367908 , version 1 (03-12-2019)

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  • HAL Id : hal-02367908 , version 1

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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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