https://hal.telecom-paris.fr/hal-02367908Elgui, KevinKevinElguiBianchi, PascalPascalBianchiS2A - Signal, Statistique et Apprentissage - LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom ParisPortier, FrançoisFrançoisPortierS2A - Signal, Statistique et Apprentissage - LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom ParisIsson, OlivierOlivierIssonLearning Methods for RSSI-based Geolocation: A Comparative StudyHAL CCSD2019Index Terms-LPWA Networkmaximum likelihoodLPWA Networkmaximum likeli- hoodlocalizationmetric learning[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Elgui, Kevin2019-12-03 15:37:132022-06-25 21:13:272019-12-03 15:38:45enConference papersapplication/pdf1In 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.