Ridge regression and Kalman filtering for target tracking in wireless sensor networks

Sandy Mahfouz (1) , Farah Mourad-Chehade (1) , Paul Honeine (1) , Joumana Farah (2) , Hichem Snoussi (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 Holy-Spirit University of Kaslik [Jounieh]

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.

Mots clés: filtering theory; Kalman filters; learning (artificial intelligence); regression analysis; telecommunication computing; Kalman filtering; machine learning; radio-fingerprints database; ridge regression learning method; input RSSI information; acceleration information; position estimation; Target tracking; Wireless sensor networks; Acceleration; Vectors; Noise; Computational modeling; radio-fingerprinting; Kalman filter; ridge regression; RSSI; tracking; WSN

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Joumana Farah, Hichem Snoussi. Ridge regression and Kalman filtering for target tracking in wireless sensor networks. Proc. eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014, A Coruna, Spain. pp.237-240, ⟨10.1109/SAM.2014.6882384⟩. ⟨hal-01965991⟩