SHSNet: A Deep Learning Method for Static Human Sensing Using MIMO FMCW Radar

In human-centric applications, a common requirement is to extract information about the people present in an environment. In this paper, we introduce a static human sensing method using MIMO FMCW radar, i.e., to detect human presence and location when a person is stationary. The primary challenge for radar-based static human sensing is to separate human radar signals from environmental background. Conventional methods use signal processing techniques and handcrafted features to identify radar signals reflected from human body, which has high limitation on signal quality. We propose a deep learning model named SHSNet that automatically learn human features in radar signals from data. Our experimental results show that our method has superior accuracy in detecting human presence, identifying empty room, and localizing individuals.