This paper proposes a novel 3D single-object tracker to more stably,  accurately, and faster track objects, even if they are temporarily missed. Our idea is to utilize spatial-temporal data association to achieve object tracking robustly, and it consists of two main parts.  We firstly employ a temporal motion model cross frames to estimate the object's temporal information and update the region of interest(ROI). The advanced detector only focuses on ROI rather than the whole scene to generate the spatial position. Second, we introduce a new pairwise evaluation system to exploit spatial-temporal data association in point clouds. The proposed evaluation system considers detection confidence, orientation offset, and objects distance to more stably achieve object matching. Then, we update the predicted state based on the pairwise spatial-temporal data. Finally, we utilize the previous trajectory to enhance the accuracy of static tracking in the refinement scheme. Experiments on the KITTI and nuScenes tracking datasets demonstrate that our method outperforms other state-of-the-art methods by a large margin (a 10\% improvement and 280 FPS on a single NVIDIA 1080Ti GPU). Compared with multi-object tracking, our tracker also has superiority.