Conventional mirror support structures often fail to meet optical system tolerances due to inadequate mitigation of the installation stresses caused by machining-induced surface irregularities and inclinations. This study proposes a novel single-point central support using an arc-shaped flexible hinge. Materials for the mirror and support structure are carefully selected based on their properties, and the optimal dimensions are determined through stiffness analysis. The new flexible support structure is detailed in this work. Simulations show that the mirror assembly achieves a first-order frequency of 296.66 Hz. Under separate triaxial gravitational and thermal loads at −40 °C and 50 °C, the mirror’s surface figure accuracy RMS remains better than λ/45 (λ = 632.8 nm). With a 0.008 mm forced displacement applied to the installation surface, the RMS change in the mirror’s surface figure is less than λ/200, meeting structural design specifications. In practical assembly tests, even when surface irregularities exceed tolerances by more than double, the RMS change in the mirror’s surface figure is only 7 nm. This design effectively ensures high-precision surface quality while significantly reducing assembly difficulty and installation surface precision requirements, achieving a large-tolerance assembly. It provides a valuable reference for mirror support design in imaging optical systems.

With the growing global emphasis on space resources, the significance of space detection and surveillance technologies has escalated. Currently, space-based optical surveillance stands as the primary means for acquiring information on space objects. However, constrained by the diffraction limits of space telescopes, distant space objects are typically imaged as point sources. The resulting lack of sufficient spatial resolution renders traditional image-based recognition algorithms ineffective. In contrast, the Bidirectional Reflectance Distribution Function (BRDF) fully characterizes surface light scattering properties through four-dimensional features, significantly outperforming traditional two-dimensional spectral techniques in material identification. Consequently, leveraging BRDF signatures at varying phase angles has emerged as an effective approach for Space Object Identification. In this study, we developed an automated BRDF measurement system to characterize various typical aerospace materials and investigated the BRDF properties of mixed-material surfaces. A material composition ratio prediction model was constructed based on a One-Dimensional Convolutional Neural Network (1D-CNN). This model effectively extracts key features, including local slope variations and global waveform characteristics, from the BRDF curves. Experimental results demonstrate that the model achieves a maximum relative percentage error of 6.21%, implying a prediction accuracy for mixed-material composition ratios consistently exceeding 93.79%. Compared to image classification methods based on remote sensing imagery, the proposed approach offers higher computational efficiency, significantly reduced model complexity and computational cost, and enhanced robustness. This work provides essential data support for material identification by space-based telescopes and establishes an algorithmic and experimental foundation for intelligent space situational awareness systems.