An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-in-space 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.

提出了一种人工智能增强型恒星识别算法，用于空间丢失模式的恒星跟踪器。在人工智能算法中使用基于Vgg16的卷积神经网络模型对恒星图像进行分类。构建训练数据集以实现网络的最佳性能。仿真结果表明，该算法对多种噪声具有很高的鲁棒性，包括位置噪声，量级噪声，假星和跟踪器的角速度。借助深层卷积神经网络，尽管有噪声和干扰，识别精度仍保持在96％，这是对传统金字塔和网格算法的重大改进。