Date of Original Version
Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico, August 2003.
Abstract or Table of Contents
This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11b network. Our approach uses data labeled by ground truth position to learn a probabilistic 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.