Privacy-preserving indoor localization via light transport analysis

Abstract

We propose a system for indoor localization using intensity-controllable LED light fixtures and light sensors mounted on the ceiling. While providing accurate location estimates, our approach preserves user privacy and is robust to ambient light conditions. We develop a LASSO algorithm and a localized ridge regression algorithm for locating a single object. In synthetic experiments, our localized ridge regression algorithm achieves an average localization error ranging from 0.24in to 1.39in, for different object sizes, in a 7×12-foot room. The localized ridge regression algorithm also shows the ability to locate multiple objects in experiments with a real-world occupancy scenario.