Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil.
Graduate Program in Applied Computer Science, University of Vale do Itajaí, Itajaí 88302-901, Brazil.
Sensors (Basel). 2021 Sep 26;21(19):6419. doi: 10.3390/s21196419.
The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).
在航天器导航中，估计参考系之间的方向至关重要。已经通过遵循代数方法构建了此类问题的稳健算法，但由于其随机性，数据驱动的解决方案变得更具吸引力。因此，本文提出了一种基于卷积神经网络的方法，以处理静态姿态确定问题中的测量不确定性。PointNet 模型使用包含不同数量观测向量的不同数据集进行训练，这些观测向量用于构建姿态剖面矩阵，作为系统的输入。在选择最佳模型时，考虑了测试场景中测量的不确定性。与传统算法（如奇异值分解（SVD）、q 方法、四元数估计器（QUEST）和最优四元数的第二估计器（ESOQ2））相比，使用卷积神经网络的提出的模型对更高的噪声不太敏感。
