From Spatial Domain to Temporal Domain: Unleashing the Capability of CFAR for mmWave Point Cloud Generation

Point clouds are a crucial data type for mmWave radar and are widely used in sensing applications such as human tracking and activity recognition. However, for indoor human sensing, the point clouds obtained by mmWave radar are often sparse. Previous studies attribute the sparsity to the limited sensing capabilities of mmWave radar, underestimating the impact of CFAR---a key algorithmic component of mmWave systems---on point cloud quality. Through empirical studies, we find that the spatial-based CFAR widely used in existing works suffers from a severe energy masking issue. This is because these algorithms work well when the target is far away enough to be approximated as a point. In short-range indoor sensing, the human body can not be considered as a point but an extended target, causing the spatial-based CFAR to calculate the noise power wrongly and accordingly a miss-generation of the point cloud. To fundamentally solve the problem, this paper proposes a temporal-based CFAR named ETCM-CFAR. We address multiple issues such as lacking initial noise power and the absence of a closed-form threshold solution to make the proposed algorithm work. Based on ETCM-CFAR, this paper proposes a point cloud generation system named mmPC. mmPC is implemented on three different types of commercial-off-the-shelf mmWave radars and extensive experiments demonstrate that mmPC significantly improves point cloud quality, increasing the number of cloud points by 148.6% compared to the state-of-the-art systems. Two representative sensing applications, i.e., fitness activity recognition and human-pet classification are further employed to demonstrate the effectiveness of mmPC on sensing.