Passive Motion Detection via mmWave Communication System

TL;DR

This paper presents a 60 GHz mmWave system that combines passive sensing and communication for hand gesture recognition, utilizing beamforming and neural networks to achieve high classification accuracy.

Contribution

It introduces an integrated passive sensing-communication system with a novel dual-beam approach and an empirical model linking sensing duration to classification accuracy.

Findings

High gesture classification accuracy with sufficient sensing time

Effective use of LoS and NLoS paths for sensing

Empirical model correlating accuracy and sensing duration

Abstract

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.