  This paper explores the use of ambient radio frequency (RF) signals for human
presence detection through deep learning. Using WiFi signal as an example, we
demonstrate that the channel state information (CSI) obtained at the receiver
contains rich information about the propagation environment. Through judicious
pre-processing of the estimated CSI followed by deep learning, reliable
presence detection can be achieved. Several challenges in passive RF sensing
are addressed. With presence detection, how to collect training data with human
presence can have a significant impact on the performance. This is in contrast
to activity detection when a specific motion pattern is of interest. A second
challenge is that RF signals are complex-valued. Handling complex-valued input
in deep learning requires careful data representation and network architecture
design. Finally, human presence affects CSI variation along multiple
dimensions; such variation, however, is often masked by system impediments such
as timing or frequency offset. Addressing these challenges, the proposed
learning system uses pre-processing to preserve human motion induced channel
variation while insulating against other impairments. A convolutional neural
network (CNN) properly trained with both magnitude and phase information is
then designed to achieve reliable presence detection. Extensive experiments are
conducted. Using off-the-shelf WiFi devices, the proposed deep learning based
RF sensing achieves near perfect presence detection during multiple extended
periods of test and exhibits superior performance compared with leading edge
passive infrared sensors. Comparison with existing RF based human presence
detection also demonstrates its robustness in performance, especially when
deployed in a completely new environment.
