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