{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:40:04Z","timestamp":1761676804152,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T00:00:00Z","timestamp":1578268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013Shafer 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\u2019s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s20010318","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T10:34:46Z","timestamp":1578306886000},"page":"318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Evidential Framework for Localization of Sensors in Indoor Environments"],"prefix":"10.3390","volume":"20","author":[{"given":"Daniel","family":"Alshamaa","sequence":"first","affiliation":[{"name":"Charles Delaunay Institute, University of Technology of Troyes, 10300 Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5048-9372","authenticated-orcid":false,"given":"Farah","family":"Mourad-Chehade","sequence":"additional","affiliation":[{"name":"Charles Delaunay Institute, University of Technology of Troyes, 10300 Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3042-183X","authenticated-orcid":false,"given":"Paul","family":"Honeine","sequence":"additional","affiliation":[{"name":"LITIS Lab, University of Rouen Normandie, 76130 Rouen, France"}]},{"given":"Aly","family":"Chkeir","sequence":"additional","affiliation":[{"name":"Charles Delaunay Institute, University of Technology of Troyes, 10300 Troyes, France"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1109\/JSEN.2017.2767066","article-title":"A Robust Single GPS Navigation and Positioning Algorithm Based on Strong Tracking Filtering","volume":"18","author":"Xiong","year":"2018","journal-title":"IEEE Sens. 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