New Star-Pattern Identification Using a Correlation Approach for Spacecraft Attitude Determination

Abstract

A new star-pattern identification algorithm using correlation pattern-matching is proposed in this study. The new approach is based upon maximizing the target cost function, which is formed by the correlation between an original image and a target image. The image is reconstructed from the centroid positions of stars that are modeled as two-dimensional Gaussian functions. The correlation function in the form of cross-convolution in the image plane can be expressed by Fourier transform, so it is constructed analytically using only the centroid positions of stars in the image plane. The proposed algorithm compared with conventional pattern-matching techniques is simpler and more reliable, as verified by simulation study.