Paper Info
Title
Never Use Labels: Signal Strength-Based Bayesian Device-Free Localization in Changing Environments.
Abstract
Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network. Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a personu0027s location, and maintaining an accurate model as the environment changes over time. Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location. Both need to be frequently recalibrated or retrained to stay current with changing environments. Other DFL methods only localize people in motion. In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a personu0027s location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes. We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements. We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by 11-51% compared to other DFL methods.
Year
DOI
Venue
2018
10.1109/tmc.2019.2901782
IEEE Transactions on Mobile Computing
Field
DocType
Volume
Wireless network,Pattern recognition,Computer science,Device free localization,Artificial intelligence,Signal strength,Labeled data,RSS,Mixture model,Calibration,Distributed computing,Bayesian probability
Journal
abs/1812.11836
Citations 
PageRank 
References 
0
0.34
17
Authors
2
Name
Order
Citations
PageRank
Peter Hillyard184.01
Neal Patwari23805241.58