Device-free positioning (DFP) using mmWave signals is an emerging technique that could track a target without attaching any devices. It conducts position estimation by analyzing the influence of targets on their surrounding mmWave signals. With the widespread utilization of mmWave signals, DFP will have many potential applications in tracking pedestrians and robots in intelligent monitoring systems. State-of-the-art DFP work has already achieved excellent positioning performance when there is one target only, but when there are multiple targets, the time-varying target state, such as entering or leaving of the wireless coverage area and close interactions, makes it challenging to track every target. To solve these problems, in this paper, we propose a spatial-temporal analysis method to robustly track multiple targets based on the high precision mmWave point cloud information. Specifically, we propose a high precision spatial imaging strategy to construct fine-grained mmWave point cloud of the targets, design a spatial-temporal point cloud clustering method to determine the target state, and then leverage a gait based identity and trajectory association scheme and a particle filter to achieve robust identity-aware tracking. Extensive evaluations on a 77 GHz mmWave testbed have been conducted to demonstrate the effectiveness and robustness of our proposed schemes.