A learning algorithm for localizing people based on wireless signal strength that uses labeled and unlabeled data

Authors:
Mary Berna;Brennan Sellner;Brad Lisien;Sebastian Thrun;Geoffrey Gordon;Frank Pfenning
Affiliations:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

Citing 2
Cited 4

Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
Robust Monte Carlo localization for mobile robots

Artificial Intelligence

Reducing the Calibration Effort for Probabilistic Indoor Location Estimation

IEEE Transactions on Mobile Computing
A taxonomy for radio location fingerprinting

LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Wireless local area network positioning

Ambient Intelligence for Scientific Discovery
An empirical characterization of radio signal strength variability in 3-d IEEE 802.15.4 networks using monopole antennas

EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.1 lb 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.