Design Optimization of a Digital Sun Sensor for Use With Parametric Processing

In this paper, we use genetic algorithms (GAs) as a heuristic for optimizing the illumination pattern for a single-axis digital sun sensor. Previous work has demonstrated that parametric algorithms can be used to provide better estimates of sun position than conventional centroiding techniques. The performance of these algorithms depends, in part, on the illumination pattern on the detector. Using a linear-phase superresolution technique that is combined with GA, we alter the number, shape, and placement of illuminating features. The GA estimator discovered high-fitness solutions that offer threefold to fivefold improvements over the baseline sensor design. We contend that these multiple peak patterns can greatly improve the performance of the sun sensor when they are coupled with parametric methods for sun position estimation. The optimal illumination pattern can be implemented, at minimal cost, by fabricating a replacement aperture mask.