UMIMO: Universal Unsupervised Learning for Mmwave Radar Sensing With MIMO Array Synthesis

Millimeter-wave (mmWave) radar sensing powered by deep learning is now emerging in numerous applications, which are predominantly trained in a supervised manner. However, due to the non-interpretable nature of mmWave signals, labeling the radar data has always been a difficult task. While there have been investigations on unsupervised pre-training for mmWave radar sensing, these methods are tailored to specific signal representations. In this paper, we propose UMIMO, an unsupervised learning framework combining the hardware nature of MIMO radar and deep learning techniques to resolve the challenge raised by the insufficient labeled data. UMIMO leverages the antenna arrays synthesized from multiple transmitting and receiving antennas in mmWave radar to construct positive samples for contrastive learning. To achieve this, we propose the constraints on angular resolution and grating lobes to generate effective signal representations with different synthetic arrays. We conduct experiments using UMIMO on three tasks: contactless ECG monitoring, 3D human pose estimation, and human silhouette generation. All experimental results demonstrate that UMIMO can effectively improve the performance of learning-based mmWave radar sensing in an unsupervised manner.