Robust and Efficient Star Identification Algorithm based on 1-D Convolutional Neural Network
Shaofei Yang, Longjun Liu, Jiantao Zhou, Yunfu Zhao, Gengxin Hua, Hongbin Sun, Nanning Zheng
Abstract
As the core of the attitude determination system, the star sensor working in “lost in space” scenarios requires the star identification algorithm to be robust and fast with limited computing and memory resources. Nevertheless, previous algorithms are not satisfactory in robustness and identification speed. Hence, motivated by the fact that the one-dimensional convolutional neural network (1D-CNN) is suitable for sequential data, this article proposes a robust and efficient star identification algorithm, where 1D-CNN is used to process mixed initial features from star points. Moreover, this article proposes a combined star points selection strategy technique and a mixed initial features extraction technique to further improve the performance of 1D-CNN-based algorithm. Experimental results show that, compared with the state-of-the-art algorithm, the proposed algorithm can improve the average identification accuracy by 0.76%, the identification speed by 1.86× with the comparable memory consumption.