RIDGE REGRESSION AND KALMAN FILTERING FOR TARGET TRACKING IN WIRELESS SENSOR NETWORKS

被引：0

作者：

Mahfouz, Sandy ^{[1
]}

Mourad-Chehade, Farah ^{[1
]}

Honeine, Paul ^{[1
]}

Farah, Joumana ^{[2
]}

Snoussi, Hichem ^{[1
]}

机构：

[1] Univ Technol Troyes, Inst Charles Delaunay, CNRS, Troyes, France

[2] Univ St Esprit Kaslik, Fac Engn, Dept Telecommun, Juniyah, Lebanon

来源：

2014 IEEE 8TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM) | 2014年

关键词：

radio-fingerprinting; Kalman filter; ridge regression; RSSI; tracking; WSN;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：237 / 240

页数：4

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