CNN-based Star Tracker For High Precision Spacecraft Navigation

Abstract

There are several sensors to detect the attitude determination of the spacecraft. One of the most accurate is the star tracker which is optical equipment that measures the positions of stars. However traditional star identification algorithms are limited by their processing speed and may fail under harsh conditions which can be critical for the spacecraft. Furthermore a large and complex database of star trackers can cause inefficiencies throughout the processing process. An improved star tracker algorithm is proposed to address the issues above. This algorithm will be able to handle these challenging situations and give the most accurate estimation of the spacecraft orientation using state-of-the-art detection. This paper proposes an improved star detection algorithm for spacecraft attitude detection using convolutional neural networks (CNN), a type of deep learning algorithm that has shown many promising results in various image processing applications. CNN-based star detection algorithm will be able to identify the stars based on the constellations and show the spacecraft's orientation using a less complex database. This will positively affect the accuracy and efficiency of the processing and increase robustness against challenging conditions. Using CNN-based algorithms creates an opportunity continuously improve spacecraft attitude control systems. Free, open-source databases are being used to train the algorithm to overcome the issue of inaccessible star and constellation images from satellites. Publicly available data from planetariums such as Stellarium and space simulator platforms such as Celestia are being used for training, evaluation, and performance analysis of the proposed algorithm. Moreover various optimization techniques are implemented to improve the performance of the spacecraft attitude determination algorithm. It is believed that this approach will surpass the performance of the traditional algorithm and will lead to a promising direction for the development of advanced star tracker systems. Further research can focus on applying this approach to other areas of spacecraft control such as orbit determination and maneuver planning.