A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 1427-1427.
Abstract
This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11 b network. Our approach uses data labeled by ground truth position to learn a probabi listic mapping from locations to wireless signals, represented by piecewise linear Gaussians. It then uses sequences of wireless signal data (without position labels) to acquire motion models of individual people, which further improves the localization accuracy. The approach has been implemented and evaluated in an office environment.
@inproceedings{ijcai2003p224,
title = {A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data},
author = {Thrun, Sebastian and Gordon, Geoffrey J. and Pfenning, Frank and Berna, Mary and Sellner, Brennan and Lisien, Brad},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {1427--1427},
year = {2003},
url = {https://ijcai.org/proceedings/2003/224},
}
TY - CPAPER TI - A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data AU - Thrun, Sebastian AU - Gordon, Geoffrey J. AU - Pfenning, Frank AU - Berna, Mary AU - Sellner, Brennan AU - Lisien, Brad T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 1427 EP - 1427 UR - https://ijcai.org/proceedings/2003/224 ER -Copied
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