基于姿态加权核回归的航天器姿态估计

针对航天器相对姿态估计问题，提出了一种用于单目视觉成像系统的姿态估计方法。在传统核回归方法的基础上，采用训练数据在姿态空间的相似性对视觉输入（图像特征）空间的核函数进行加权，从而学习得到输入变量（图像特征）与目标变量（姿态）的联合概率分布函数，称为接受函数。对于包含未知姿态航天器的图像，通过求取接受函数在姿态空间的最大值，得到目标航天器的姿态估计值。该方法仅需要训练数据学习模型，较其他基于视觉的方法限制更少.对比实验结果证明了该方法在姿态估计方面的优越性，卫星数据集上的实验结果验证了该方法用于航天器姿态估计的有效性。

Spacecraft attitude estimation based on attitude-weighted kernel regression

An attitude estimation method for monocular vision imaging systems was proposed to solve relative attitude estimation problem of spacecrafts. On the basis of the original kernel regression model, the similarity of training data in the attitude space was used to weight kernel functions of the original kernel regression model in the visual input (image feature) space. A joint probability distribution function of input variables (image features) and target variables (attitudes) was learned, which was called acceptance function. Given images containing spacecrafts with unknown attitudes, the estimated attitude of the target spacecraft can be obtained by maximizing the acceptance function in the attitude space. The proposed method just needs training data to learn the model, so it has fewer limits than other vision based methods. Comparison experimental results show the advantage of the proposed method in attitude estimation. The effectiveness of the proposed method for spacecraft attitude estimation was also validated by the experimental results on satellite image dataset.