Current developments in various technological fields show an increasingly important role for robotic systems. The medical world is no exception with clear examples like high-tech surgery- and service robots. To be able to generate an accurate 3D reconstruction, it is critical that the 3-dimensional positions and orientations of the detector and the positions of the X-ray source during a movement are accurately known. In order to measure exact positions of the X-ray source and detector, usually external measurements are required. Using IMU sensors, measurements can be performed on linear accelerations, rotational velocities and even angles using a magnetometer. Estimations of linear position displacements are difficult to obtain using a double integration over time of the acceleration measurements due to systematic errors (drift). Additional filters are required, i.e., Kalman filters. The estimation of linear displacement proves to be the most problematic, which leads to the choice for this particular setup. The printer has a single DOF (linear translation), which is subject to (nonlinear) disturbances in the form of friction. The challenge is to model the system according to the needs for various filters and estimate the position of the printer head using specifically observed signals (typically the acceleration of the print head). The position measurement available on the system is only used for verification purposes. To correctly estimate the displacement when only using an acceleration signal an accurate model of the system is required. The system is a motion system with nonlinear friction of which the influence should be found. If the influence is significant the estimation will have to take into account this nonlinearity in order to estimate the displacement.