Satellite Attitude Estimation Based on Whole-to-Part Decoupling

Satellite attitude estimation is a key technology in on-orbit servicing missions. However, occlusions on the satellite's surface in space introduce local omissions in satellite images, resulting in incomplete extraction of global satellite features. This causes significant biases in the mapping from features to attitude. Besides, there is a coupling between the parameters in rotation representations such as Euler angles and quaternions, making it difficult to improve the accuracy of each attitude parameter simultaneously. Existing methods focus on improving feature extraction performance and lack an evaluation of the relationship between satellite structure and attitude. To address these issues, we proposed a satellite attitude estimation method based on whole-to-part decoupling to separate features and reconstruct rotation representation, balancing attitude estimation biases. Specifically, we constructed a multibranch mapping with feature decoupling to select robust local features that contribute to attitude estimation, mitigating the impact of occlusion on feature extraction. Meanwhile, we designed an explicit rotation representation to decouple attitude parameters and match the relevant mappings for each parameter, reducing the estimation biases. The experimental results on public datasets demonstrate that the proposed method outperforms existing methods.