Optical star sensors provide high-precision attitude determination for astronomical navigation, and daytime star sensors further enable all-time operation by breaking through traditional daytime constraints. However, intense daytime atmospheric radiation, along with optical lens and detector imperfections, reduces the signal-to-noise ratio (SNR) of dim star spots and causes numerous false alarms, which cannot be effectively addressed by traditional spatial feature-based methods. To tackle this problem, this paper proposes a multi-stream feature fusion framework integrated with a frame difference-optical flow guided spatial–temporal enhancement module, which supplements motion-aware features and adopts attention mechanisms for noise suppression. Validated via synthetic dataset training and ground-based experiments on real star maps, the proposed approach achieves superior daytime star detection performance, thus enhancing the robustness of star sensors in harsh optical environments.
zhong et al. (Mon,) studied this question.