Radiation therapy (RT) of lung tumors requires an accurate and real-time localization of the tumor while the patient is being treated. Tumor motion caused by breathing can impact dose delivery in patients with lung cancer, leading to poor disease management and damage to surrounding normal tissues. A major challenge in tumor tracking using fluoroscopic imaging is to track the tumor while it is occluded by the overlapping bones (ribs and spine). In this work, we propose a series of three modeling strategies combining template matching for initial probability estimation and Kalman filter for sequential measurement updates. The images were acquired using a fast kV switching real-time fluoroscope utilizing a dynamic thorax motion phantom. We utilized three distinct physics-based state representations to predict the motion of the tumor: Velocity, Acceleration, and Spring Motion. Additionally, occlusion and noise were simulated with general improvements from the Spring Motion Kalman filter. The proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow.