Highly Robust Lost-in-Space Algorithm Based on the Shortest Distance Transform

Abstract

A robust and fast algorithm that solves the lost-in-space problem for star trackers is presented in this paper. The algorithm is based on an image-processing technique, the shortest distance transform, which transforms the camera image into a two-dimensional lookup table. The information from the database can then be efficiently inserted into this table to compare the camera image with the database. This approach results in an algorithm that is robust to false stars, distortions on star positions, and failure of registration of bright stars. As an example, the algorithm determines over 99% of camera images correctly when 400 false stars are added, distortions of 300 arcseconds are present, and the brightest star is missing in the image. In case of incorrect determination, a very reliable criterion indicates that the determination step has to be repeated. The robustness of this algorithm can allow the use of star trackers in hostile environments. Furthermore, the algorithm is a valuable contribution to the expanding field of small satellite projects where the low-cost camera components are more prone to error and registration of false stars. Small satellites using this algorithm can acquire great functionality at low component costs.