  In this paper, an integrated passive sensing and communication system working
in 60 GHz band is elaborated, and the sensing performance is investigated in an
application of hand gesture recognition. Specifically, in this integrated
system, there are two radio frequency (RF) chains at the receiver and one at
the transmitter. Each RF chain is connected with one phased array for analog
beamforming. To facilitate simultaneous sensing and communication, the
transmitter delivers one stream of information-bearing signals via two beam
lobes, one is aligned with the main signal propagation path and the other is
directed to the sensing target. Signals from the two lobes are received by the
two RF chains at the receiver, respectively. By cross ambiguity coherent
processing, the time-Doppler spectrograms of hand gestures can be obtained.
Relying on the passive sensing system, a dataset of received signals, where
three types of hand gestures are sensed, is collected by using Line-of-Sight
(LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
Then a neural network is trained by the dataset for motion detection. It is
shown that the classification accuracy rate is high as long as sufficient
sensing time is assured. Finally, an empirical model characterizing the
relation between the classification accuracy and sensing duration is derived
analytically.
