# Never Use Labels: Signal Strength-Based Bayesian Device-Free Localization in Changing Environments

## TL;DR

This paper introduces a novel Bayesian device-free localization method that uses a mixture model of RSS signals, enabling accurate localization in changing environments without labeled calibration data.

## Contribution

The authors propose a new mixture model for RSS-based localization and a framework for updating model parameters without labeled data, improving robustness in dynamic environments.

## Key findings

- Localization error reduced by 11-51% compared to existing methods
- System maintains performance without labeled calibration in changing environments
- Validated across three sites with seven days of measurements

## 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 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.