Enhancement of the Centroiding Algorithm for Star Tracker Measure Refinement

The application of the hyperacuity technique to image processing of star trackers is analysed. An analytical study of the error introduced by the centroiding algorithm is presented and it is shown that a systematic contribution and a random one exist. They result from image processing assumptions and photometric measure uncertainty, respectively. Their behaviour is characterised by means of numerical simulations that are based on optics theoretical point spread functions. The latter take into account both defocus and diffraction effects. First, measured star position uncertainty is evaluated as a function of defocus. As a result, a criterion for optimal defocus is presented. Subsequently, an original procedure for systematic centroiding error correction by means of a backpropagation neural network is described. It is also suitable for real hardware calibration. When applied to one of the considered numerical models, the position computation accuracy is improved from 0.01 to 0.005 pixels.