Real-Time Through-Wall Multihuman Localization and Behavior Recognition Based on MIMO Radar

Human localization and behavior recognition (HLBR) is an important research topic in wireless sensing and computer vision. Most studies mainly use cameras and millimeter-wave radars, which can achieve fine-grained human localization; however, they cannot solve the problem of wall occlusion. Although methods that use Wi-Fi can penetrate walls, they cannot detect the precise localization of people due to their narrow bandwidth limitation. The above problems limit the application of HLBR in real life. This article proposes a real-time, through-wall, multi-HLBR (RTWLBR) system, RTWLBR. In this system, we design a multiple-input–multiple-output (MIMO) radar in the 1–2-GHz frequency range. An ultrawideband (UWB) positioning device and a camera are attached to the self-developed, through-wall radar system to capture the simultaneous localization, behavior information, and radar reflection heatmap of people behind a wall, where location information and behavior information are jointly embedded and encoded as a confidence matrix, which serves as a supervision signal for neural network training. A multifeature fusion network based on a 3-D convolutional neural network (3-D CNN) is designed to learn human localization and behavior information from radar heatmaps. A joint heatmap compression autoencoder network is utilized to assist network training to reduce quantization error and running costs. The experimental results show that the RTWLBR method can perform real-time localization and behavior recognition of target humans behind a 24-cm brick wall.