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