Name
Title | ||
|---|---|---|
Ridge regression and Kalman filtering for target tracking in wireless sensor networks |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SAM.2014.6882384 | Sensor Array and Multichannel Signal Processing Workshop |
Keywords | Field | DocType |
Kalman filters,filtering theory,learning (artificial intelligence),regression analysis,target tracking,telecommunication computing,wireless sensor networks,Kalman filtering,acceleration information,input RSSI information,machine learning,position estimation,radio-fingerprints database,ridge regression learning method,target tracking,wireless sensor networks,Kalman filter,RSSI,WSN,radio-fingerprinting,ridge regression,tracking | Computer vision,Regression,Fast Kalman filter,Computer science,Ridge,Computer network,Kalman filter,Acceleration,Artificial intelligence,Wireless sensor network,Machine learning | Conference |
ISSN | Citations | PageRank |
1551-2282 | 1 | 0.35 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahfouz, S. | 1 | 15 | 2.04 |
| Mourad-Chehade, F. | 2 | 4 | 1.10 |
| Honeine, P. | 3 | 11 | 1.92 |
| Farah, J. | 4 | 11 | 2.06 |