Centroid extraction algorithm based on grey-gradient for autonomous star sensor

Abstract

The noise and complicated working environment experienced by the star sensor during spacecraft applications both have negative effects on the star image quality, which consequently degrades centroid extraction precision. This study proposes a novel centroid extraction algorithm to improve the precision and anti-interference performance. First, the block adaptive threshold segmentation (BATS) algorithm is proposed as a preliminary method for reducing star image noise. Then, to overcome problems caused by overlapping star spots and to provide dependable seed points for the local region growth (LRG) method, a grey-gradient-based greyscale cross-projection method (GGCPM) is proposed. Finally, LRG is combined with Gaussian surface fitting method to accurately extract the centroid since the edge of star spot could be maintained to further improve centroid extraction accuracy. Numerical simulation results demonstrate that the proposed algorithm has higher precision and better anti-interference performance than several traditional centroid extraction algorithms, and an actual star image application showed the algorithm is reliable and stable when applied in an actual environment.