Target tracking using machine learning and Kalman filter 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 describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The target's accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end, radio-fingerprints of received signal strength indicators (RSSIs) are first collected over the surveillance area. The obtained database is then used with machine learning algorithms to compute a model that estimates the position of the target using only RSSI information. This model leads to a first position estimate of the target under investigation. The kernel-based ridge regression and the vector-output regularized least squares are used in the learning process. The Kalman filter is used afterward to combine predictions of the target's positions based on acceleration information with the first estimates, leading to more accurate ones. The performance of the method is studied for different scenarios and a thorough comparison with well-known algorithms is also provided.

Mots clés: wireless sensor networks; Kalman filters; learning (artificial intelligence); vector-output regularized least squares; kernel-based ridge regression; machine learning algorithms; received signal strength indicators; radio-fingerprints; Kalman filter; Acceleration; Vectors; Target tracking; Sensors; Covariance matrices; Mathematical model; State-space methods; Radio-fingerprinting; machine learning; RSSI

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Joumana Farah, Hichem Snoussi. Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sensors Journal, 2014, 14 (10), pp.3715 - 3725. ⟨10.1109/JSEN.2014.2332098⟩. ⟨hal-01965571⟩