Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [20 references]  Display  Hide  Download

https://hal.telecom-paris.fr/hal-02367908
Contributor : Kevin Elgui <>
Submitted on : Tuesday, December 3, 2019 - 3:37:13 PM
Last modification on : Friday, October 16, 2020 - 4:55:12 PM
Long-term archiving on: : Wednesday, March 4, 2020 - 1:26:36 PM

File

Learning_Methods_for_RSSI_base...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02367908, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

110

Files downloads

259