Privacy-Preserving Indoor Localization Via Light Transport Analysis

We propose a system for indoor localization using intensitycontrollable 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 1.39 in, for different object sizes, in a 7 x 12-foot room. The localized ridge regression algorithm also shows the ability to locate multiple objects in experiments with a real-world occupancy scenario.