When telescopes are exploited for the observation of orbiting objects, images are often distorted by diurnal motion and also by the motion of the imaging apparatus during acquisition, causing a significant drift across image sequences. The drift of normally static objects, such as the stars in the background, can be exploited to correct the effect of the drift and recalibrate the sequence of images. This paper presents recent developments in multitarget detection and tracking techniques, exploiting the single cluster Probability Hypothesis Density (PHD) filter, in order to jointly estimate the static objects and the sensor drift. A comparison on the correction of the sensor drift is carried out between the PHD filter, the Second-Order Probability Hypothesis Density (SO-PHD) filter, the Cardinalized Probability Hypothesis Density (CPHD) filter, and the pattern matching, an established technique for the correction of image drift in astronomical data