Metric Learning for Fingerprint RSSI-Localization

Kevin Elgui, Pacal Bianchi, Olivier Isson, Francois Portier, Renaud Marty

Peer Reviewed

Abstract: In this paper, we describe a framework dedicated to the geolocation of devices that can only be positioned in a set of specific locations called points of interest (noted PoIs). After a short introduction explaining the importance of this topic, a machine learning approach of this problem will be formalized and some of the off-the-shelf predictors that can be used to solve this geolocation problem will be discussed. Based on this review, the k-nearest neighbors (k-NN) method appears interesting for business applications due to its simplicity and reasonable effectiveness. We will then show that a gradient boosting metric learning enables to improve the k-NN weights and therefore leads to better performances with respect to the classical Euclidean distance choice for the similarity metric. We will discuss the effectiveness of this approach in our case consisting of a RSSI-localization task in high a dimensional space.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 1036 - 1042
Cite this article: Elgui, Kevin, Bianchi, Pacal, Isson, Olivier, Portier, Francois, Marty, Renaud, "Metric Learning for Fingerprint RSSI-Localization," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1036-1042.
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