Observation satellites use pushbroom sensors to capture images of the earth. These linear cameras acquire 1-D images over time and use the straight motion of the satellite to sweep out a region of space and build 2-D images. The stability of the imaging platform is crucial during the acquisition process to guaranty distortion free images. Positioning sensors are used to control and rectify the attitude variations of the satellite, but their sampling rate is too low to provide an accurate estimate of the motion. In this paper, we describe a way to fuse star tracker measurements with image registration in order to retrieve the attitude variations of the satellite. We introduce first a simplified motion model where the pushbroom camera is rotating during the acquisition of an image. Then we present the fusion model which combines low and high frequency informations of respectively the star tracker and the images; this is embedded in a Bayesian setting. Lastly, we illustrate the performance of our algorithm on three satellite datasets.