Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks. Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., & Snoussi, H. IEEE sensors journal, 16(7):2115 - 2126, April, 2016.
Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks [link]Link  Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks [pdf]Paper  doi  abstract   bibtex   
This paper introduces two main contributions to the wireless sensor network (WSN) society. The first one consists of modeling the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) exchanged by these sensors in an indoor WSN. In this context, two models are determined using a radio-fingerprints database and kernel-based learning methods. The first one is a non-parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a non-linear fluctuation term. As for the second contribution, it consists of tracking a moving target in the network using the estimated RSSI/distance models. The target's position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either the Kalman filter or the particle filter. A fully comprehensive study of the choice of parameters of the proposed distance models and their performances is provided, as well as a study of the performance of the two proposed tracking methods. Comparisons with recently proposed methods are also provided.
@ARTICLE{16.wsn.semiparam,
   author =  "Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi",
   title =  "Non-parametric and semi-parametric {RSSI}/distance modeling for target tracking in wireless sensor networks",
   journal =  "IEEE sensors journal",
   year  =  "2016",
   volume =  "16",
   number =  "7",
   pages =  "2115 - 2126",
   month =  apr,
   url_link= "https://ieeexplore.ieee.org/document/7360093",
   doi="10.1109/JSEN.2015.2510020", 
   url_paper   =  "http://honeine.fr/paul/publi/16.wsn.semiparam.pdf",
   keywords  =  "machine learning, wireless sensor networks, distance estimation, Kalman filter, machine learning, particle filter, radio-fingerprints, RSSI, target tracking, wireless sensor networks, nonparametric RSSI/distance modeling, semiparametric RSSI/distance modeling, radio-fingerprints database, kernel-based learning methods, nonparametric regression model, semiparametric regression model, log-distance theoretical propagation model, nonlinear fluctuation term, moving target tracking, received signal strength indicators",
   abstract  = "This paper introduces two main contributions to the wireless sensor network (WSN) society. The first one consists of modeling the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) exchanged by these sensors in an indoor WSN. In this context, two models are determined using a radio-fingerprints database and kernel-based learning methods. The first one is a non-parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a non-linear fluctuation term. As for the second contribution, it consists of tracking a moving target in the network using the estimated RSSI/distance models. The target's position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either the Kalman filter or the particle filter. A fully comprehensive study of the choice of parameters of the proposed distance models and their performances is provided, as well as a study of the performance of the two proposed tracking methods. Comparisons with recently proposed methods are also provided.",
}
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