This thesis evaluates the performance of a Photonic Mixer Device (PMD) camera concerning its ability to determine the relative position and orientation between a target and the PMD camera in order to provide a basic concept for using a PMD camera for spacecraft docking. Therefore, the work is divided into three main tasks, namely pre-processing, calibration, as well as poses estimation and tracking. Since the frontal image of a docking port normally has a circular or ellipse feature, the thesis introduces the RANSAC-based ellipse detection in order to determine the ellipse feature from a 2D image quickly. The relative position and orientation of the docking port is later calculated from the detected ellipse with the help of distance data. This method is referred to as the feature-based pose determination. The pose of the docking port can also derive from the 2.5D image, which is referred to as the shape-based pose determination. The well-known registration algorithm, the Iterative Closest Point (ICP) algorithm, can match the 2.5D image with the model point data of the docking port. The relative transformation vector is the result of the registration. The algorithm is initialized by the result of the feature-based pose estimation. Because of the ICP-based tracking algorithm, the shape-based pose determination is independent from the pose initialization by creating the close loop ICP pose determination. The evaluation between the feature-based and shape-based pose determination algorithm shows that the performance of the pose determination from the feature-based determination is better than from the shape-based concerning the standard deviation and the computational time. Moreover, the speed of the feature-based pose estimation is feasible to apply to space docking missions.