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：航天器位姿估计在交会对接、碎片清除及在轨维护等空间任务中至关重要。当前空间图像存在光照条件剧烈变化、高对比度与低分辨率等特点，使得空间目标位姿估计比地面物体更具挑战性。本文通过分析航天器在轨雷达图像特征，提出一种名为密集残差U形网络（DR-U-Net）的新型深度学习神经网络结构用于图像特征提取。进一步地，我们引入基于DR-U-Net的新型神经网络——航天器U形网络（SU-Net），实现非合作航天器的端到端位姿估计。具体而言，SU-Net首先对非合作航天器图像进行预处理，并采用迁移学习进行预训练；其次，为解决雷达图像模糊及航天器轮廓识别能力不足的问题，我们在主干网络U-Net中增加残差连接与密集连接，命名为DR-U-Net。该方法降低了特征损失与模型复杂度，同时避免了深度神经网络训练中的退化问题；最后，采用单层前馈神经网络对在轨非合作航天器进行位姿估计。实验证明，所提方法不依赖人工设计的目标特定特征，模型具有强鲁棒性，且计算精度优于当前最优位姿估计方法。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。