@INPROCEEDINGS{14.sam.kalman,
   author =  "Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi",
   title =  "Ridge regression and {Kalman} filtering for target tracking in wireless sensor networks",
   booktitle =  "Proc. eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)",
   address =  "A Coruna, Spain",
   year  =  "2014",
   month =  "22 - 25~" # jun,
   acronym =  "SAM",
   pages = {237-240}, 
   url_link= "https://ieeexplore.ieee.org/document/6882384",
   url_paper   =  "http://honeine.fr/paul/publi/14.sam.kalman.pdf",
   abstract={This paper introduces an original method for target tracking in wireless sensor networks that combines machine learning and Kalman filtering. A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured. This model leads to a position estimate for each target. The Kalman filter is used afterwards to combine the model's estimates with predictions of the target's positions based on acceleration information, leading to more accurate ones.}, 
   keywords={filtering theory, Kalman filters, learning (artificial intelligence), regression analysis, target tracking, telecommunication computing, wireless sensor networks, Kalman filtering, target tracking, wireless sensor networks, machine learning, radio-fingerprints database, ridge regression learning method, input RSSI information, acceleration information, position estimation, Kalman filters, Target tracking, Wireless sensor networks, Acceleration, Vectors, Noise, Computational modeling, radio-fingerprinting, Kalman filter, ridge regression, RSSI, tracking, WSN}, 
   doi={10.1109/SAM.2014.6882384}, 
   ISSN={2151-870X}, 
}

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A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured. This model leads to a position estimate for each target. The Kalman filter is used afterwards to combine the model's estimates with predictions of the target's positions based on acceleration information, leading to more accurate ones.","keywords":"filtering theory, Kalman filters, learning (artificial intelligence), regression analysis, target tracking, telecommunication computing, wireless sensor networks, Kalman filtering, target tracking, wireless sensor networks, machine learning, radio-fingerprints database, ridge regression learning method, input RSSI information, acceleration information, position estimation, Kalman filters, Target tracking, Wireless sensor networks, Acceleration, Vectors, Noise, Computational modeling, radio-fingerprinting, Kalman filter, ridge regression, RSSI, tracking, WSN","doi":"10.1109/SAM.2014.6882384","issn":"2151-870X","bibtex":"@INPROCEEDINGS{14.sam.kalman,\n author = \"Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi\",\n title = \"Ridge regression and {Kalman} filtering for target tracking in wireless sensor networks\",\n booktitle = \"Proc. eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)\",\n address = \"A Coruna, Spain\",\n year = \"2014\",\n month = \"22 - 25~\" # jun,\n acronym = \"SAM\",\n pages = {237-240}, \n url_link= \"https://ieeexplore.ieee.org/document/6882384\",\n url_paper = \"http://honeine.fr/paul/publi/14.sam.kalman.pdf\",\n abstract={This paper introduces an original method for target tracking in wireless sensor networks that combines machine learning and Kalman filtering. A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured. This model leads to a position estimate for each target. 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