Spacecraft pose estimation plays a vital role in many on-orbit space missions, such as rendezvous and docking, debris removal, and on-orbit maintenance. At present, space images contain widely varying lighting conditions, high contrast and low resolution, pose estimation of space objects is more challenging than that of objects on earth. In this paper, we analyzing the radar image characteristics of spacecraft on-orbit, then propose a new deep learning neural Network structure named Dense Residual U-shaped Network (DR-U-Net) to extract image features. We further introduce a novel neural network based on DR-U-Net, namely Spacecraft U-shaped Network (SU-Net) to achieve end-to-end pose estimation for non-cooperative spacecraft. Specifically, the SU-Net first preprocess the image of non-cooperative spacecraft, then transfer learning was used for pre-training. Subsequently, in order to solve the problem of radar image blur and low ability of spacecraft contour recognition, we add residual connection and dense connection to the backbone network U-Net, and we named it DR-U-Net. In this way, the feature loss and the complexity of the model is reduced, and the degradation of deep neural network during training is avoided. Finally, a layer of feedforward neural network is used for pose estimation of non-cooperative spacecraft on-orbit. Experiments prove that the proposed method does not rely on the hand-made object specific features, and the model has robust robustness, and the calculation accuracy outperforms the state-of-the-art pose estimation methods. The absolute error is 0.1557 to 0.4491 , the mean error is about 0.302 , and the standard deviation is about 0.065 .

翻译：航天器姿态估计在交会对接、碎片清理和在轨维护等众多在轨空间任务中起着至关重要的作用。当前空间图像包含差异极大的光照条件、高对比度和低分辨率，使得空间目标的姿态估计比地球目标更具挑战性。本文分析了航天器在轨雷达图像特征，进而提出一种名为密集残差U型网络（DR-U-Net）的新型深度学习神经网络结构以提取图像特征。我们进一步引入基于DR-U-Net的新型神经网络——航天器U型网络（SU-Net），实现非合作航天器的端到端姿态估计。具体而言，SU-Net首先对非合作航天器图像进行预处理，并使用迁移学习进行预训练；随后，为解决雷达图像模糊及航天器轮廓识别能力弱的问题，我们在骨干网络U-Net中加入残差连接和密集连接，并将其命名为DR-U-Net。通过这种方式，减少了特征损失和模型复杂度，并避免了深度神经网络在训练过程中的退化。最后，采用一层前馈神经网络进行非合作航天器在轨姿态估计。实验证明，所提方法不依赖人工设计的物体特定特征，模型具有鲁棒稳定性，且计算精度优于现有最先进姿态估计方法。绝对误差范围为0.1557至0.4491，平均误差约为0.302，标准差约为0.065。