This project is motivated by the difficulty in standard WiFi localization due to poor signal propagation within buildings due to environmental blockage and other interfering signals. Indoor localization is critical for for 6G applications such as compliant manufacturing, indoor navigation, and autonomous vehicles. The project uses machine learning and chirp signal positioning approaches to get the centimeter accuracy of location of user equipment.

Team Members

Gagan Kaushik, Jean Lee, Brian Menezes, Matthew Qin, Justin Swinney