Space Surveillance payload camera breadboard: Star tracking and debris detection algorithms

Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth's orbits. However, SST ellorts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera bread-board was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms' tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net, to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost.(c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.