  In this work we present RAPID, the first joint communication and radar system
based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz
band. Unlike existing approaches for human sensing at millimeter-wave
frequencies, which rely on special-purpose radars, RAPID achieves radar-level
sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of
installing ad-hoc sensors. RAPID enables contactless human sensing
applications, such as people tracking, Human Activity Recognition (HAR), and
person identification without requiring modifications to the standard packet
structure. Specifically, we leverage IEEE 802.11ay beam training to accurately
localize and track multiple individuals within the same environment. Then, we
propose a new way of using beam tracking to extract micro-Doppler signatures
from the time-varying Channel Impulse Response (CIR) estimated from reflected
packets. Such signatures are fed to a deep learning classifier to perform HAR
and person identification. RAPID is implemented on a cutting-edge IEEE
802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on
a large dataset of CIR measurements. It is robust across different environments
and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing
techniques. Using two access points, RAPID reliably tracks multiple subjects,
reaching HAR and person identification accuracies of 94% and 90%, respectively.
