A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data

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.

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Sebastian Thrun, Geoffrey J. Gordon, Frank Pfenning, Mary Berna, Brennan Sellner, Brad Lisien
• Keywords: Approach Uses Data, IJCAI 2003, IJCAI 2007, Probabilistic Approach, Wireless Signal