Demo: Meta2Locate: Meta Surface Enabled Indoor Localization in Dynamic Environments

Received signal strength (RSS) fingerprint map is one of the most widely-used indoor localization approaches, but it often relies on multiple access points (AP) for data collection and suffers from frequent data updates due to dynamic wireless environments. In this work, we implement a reconfigurable-intelligent-surface (RIS) assisted indoor localization system named Meta2Locate to tackle the above issues using only one AP. In the proposed system, we deploy our self-designed RIS at 5.5GHz in an indoor environment, which can customize the propagation channels between the AP and the target. For the changing propagation environment, we design a mean maximum discrepancy weighted meta-learning approach to train a model that maps the RSS fingerprint to the location of the user, and it only needs a few data for the model update.