Open AccessDesign and Optimization of Star Recognition Algorithm Based on Hierarchical CNN
Author(s) -
Wenjun Lü,
Gengxin Hua,
Yunfu Zhao,
Jiantao Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012009
Subject(s) - computer science , convolutional neural network , robustness (evolution) , algorithm , normalization (sociology) , key (lock) , artificial intelligence , database normalization , artificial neural network , pattern recognition (psychology) , machine learning , data mining , biochemistry , chemistry , computer security , sociology , anthropology , gene
With the rapid-development of AI technology, artificial intelligence algorithms for the aerospace applications have shown very good simulation performance in many areas. Among the spaceborne application fields, star identification can be seen as a typical pattern recognition process. It’s also the key part of attitude determination of the satellites, which requires the algorithm to be robust and efficient due to the limited computing and storing resources of the spaceborne computers. Nevertheless, most of the previous algorithms are not possible to be applied in practical due to the reasons above. This article proposes a strategy of constructing ‘net-structure’ images of stars to build the datasets for training and testing. Besides, a hierarchical convolutional neural network(CNN) with a small size is also designed. It performs good results on robustness and efficiency in the experiments. In the end, a method of fusing the Conv layers and the batch normalization (BN)layers is also adopted to further accelerate the algorithm.