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.

Keywords: decision-making; evidence fusion; localization; WiFi RSSI

Domains: Statistics [stat]; Machine Learning [stat.ML]; Engineering Sciences [physics]; Signal and Image processing; Mathematics [math]; Statistics [math.ST]; Computer Science [cs]; Signal and Image Processing; Computer Science [cs]; Neural and Evolutionary Computing [cs.NE]; Computer Science [cs]; Machine Learning [cs.LG]; Computer Science [cs]; Computers and Society [cs.CY]; Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]; Computer Science [cs]; Artificial Intelligence [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⟩