  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
person's 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 person's 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.
