An Evidential Framework for Localization of Sensors in Indoor Environments

Daniel Alshamaa (1) , Farah Mourad-Chehade (1) , Paul Honeine (2) , Aly Chkeir (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 DocApp - LITIS - Equipe Apprentissage

Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

Mots clés: decision-making; evidence fusion; localization; WiFi RSSI

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]; Statistiques [math.ST]; Traitement du signal et de l'image [eess.SP]; Réseau de neurones [cs.NE]; Apprentissage [cs.LG]; Ordinateur et société [cs.CY]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Daniel Alshamaa, Farah Mourad-Chehade, Paul Honeine, Aly Chkeir. An Evidential Framework for Localization of Sensors in Indoor Environments. Sensors, 2020, 20 (1), pp.318. ⟨10.3390/s20010318⟩. ⟨hal-02433601⟩