Seeing the Unseen: Passive RF Sensing via Learning

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This project explores the prevalence of wireless services and devices to extract valuable information about the ambient environment. This includes, among others, occupancy status of an indoor environment, occupant and movement/motion classification, and other high-level information that can be inferred from wireless signals. The benefits of having access to such timely and accurate situational awareness information are enormous. Real-time occupancy information is essential for intelligent and green building to reduce carbon footprint of commercial and residential buildings. Accurate motion detection, and in particular, the ability to distinguish motions between human and pets can help provide low-cost home security solutions. Autonomous fall detection in a non-intrusive and continuous manner is key to providing long-term care for the well-being of some of the most vulnerable populations.This project takes a data-driven learning approach for passive RF sensing, i.e., extracting situational awareness information of the ambient environment through judicious processing of existing radio frequency (RF) signals. Passive RF sensing has a unique set of challenges due to inherent RF impairments, environment fluctuation, and transceiver location changes, leading to divergent approaches in the literature. This project brings domain knowledge and strong expertise in wireless communication and RF propagation to passive RF sensing applications. Such domain knowledge is critical for understanding the challenges and helps inform the formulation of associated learning problems including supervised dimensionality reduction, learning with data imbalance, and learning with sampling bias. Striving for a data-driven approach, the project will build up our knowledge of the cause-and-effect relationship between environment and RF signal reception and provide theoretically sound and practically meaningful solutions to a number of passive RF sensing problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.