Anartificial intelligence enhanced star identification algorithm

Abstract

An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-inspace mode. A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images. The training dataset is constructed to achieve the networks’ optimal performance. Simulation results show that the proposed algorithm is highly robust to many kinds of noise, including position noise, magnitude noise, false stars, and the tracker’s angular velocity. With a deep convolutional neural network, the identification accuracy is maintained at 96% despite noise and interruptions, which is a significant improvement to traditional pyramid and grid algorithms.

Keywords

Star tracker / Lost-in-space / Star identification / Convolutional neural network

RIGHTS & PERMISSIONS

2020 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature