Modèle semi-paramétrique RSSI/distance pour le suivi d'une cible dans les réseaux de capteurs sans fil. Mahfouz, S.; Mourad-Chehade, F.; Honeine, P.; Farah, J.; and Snoussi, H. In Actes du 25-ème Colloque GRETSI sur le Traitement du Signal et des Images, Lyon, France, September, 2015.
Modèle semi-paramétrique RSSI/distance pour le suivi d'une cible dans les réseaux de capteurs sans fil [pdf]Paper  abstract   bibtex   
– This paper introduces two main contributions to the wireless sensor network domain. The first one consists of determining, using a semi-parametric model, the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) of the signals exchanged by these sensors in a network. As for the second contribution, it consists in tracking a moving target in the network using the estimated RSSI/distance model. The target's position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either a Kalman or a particle filter.
@INPROCEEDINGS{15.gretsi.wsn,
   author =  "Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi",
   title =  "Modèle semi-paramétrique RSSI/distance pour le suivi d'une cible dans les réseaux de capteurs sans fil",
   booktitle =  "Actes du 25-ème Colloque GRETSI sur le Traitement du Signal et des Images",
   address =  "Lyon, France",
   year  =  "2015",
   month =  sep,
   keywords  =  "machine learning, wireless sensor networks",
   acronym =  "GRETSI'15",
   url_paper  =  "http://honeine.fr/paul/publi/15.gretsi.wsn.pdf",
   abstract = "– This paper introduces two main contributions to the wireless sensor network domain. The first one consists of determining, using a semi-parametric model, the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) of the signals exchanged by these sensors in a network. As for the second contribution, it consists in tracking a moving target in the network using the estimated RSSI/distance model. The target's position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either a Kalman or a particle filter.",
}
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