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A regression model-based method for indoor positioning with compound location fingerprints / Tomofumi Takayama in Geo-spatial Information Science, vol 22 n° 2 (June 2019)  

Public [article]  inGeo-spatial Information Science > vol 22 n° 2 (June 2019) . - pp 107 - 113 Titre : A regression model-based method for indoor positioning with compound location fingerprints Type de document : Article/Communication Auteurs : Tomofumi Takayama, Auteur ; Takeshi Umezawa, Auteur ; Nobuyoshi Komuro, Auteur ; Noritaka Osawa, Auteur Année de publication : 2019 Article en page(s) : pp 107 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] Bluetooth [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] navigation à l'estime [Termes IGN] positionnement en intérieur [Termes IGN] régression Résumé : (Auteur) This paper proposed and evaluated an estimation method for indoor positioning. The method combines location fingerprinting and dead reckoning differently from the conventional combinations. It uses compound location fingerprints, which are composed of radio fingerprints at multiple points of time, that is, at multiple positions, and displacements between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning, the method uses short-range dead reckoning. The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at 30 measuring points, which were points at the intersections on a 1 × 1 m grid with no obstacles. A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them. Random Forests (RF) was used to build regression models to estimate positions from location fingerprints. The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons. This error is lower than that received with a single-point baseline model, where a feature vector is composed of only RSSI values at one location. The results suggest that the proposed method is effective for indoor positioning. Numéro de notice : A2019-324 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1612599 Date de publication en ligne : 17/05/2019 En ligne : https://doi.org/10.1080/10095020.2019.1612599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93324 [article]