Space-based optical detection is a critical capability for Space Situational Awareness, yet the scarcity of real on-orbit observation data significantly hampers the development and validation of object detection and tracking algorithms. To address this need, this paper proposes a high-fidelity image simulation method designed to provide reliable data sup-port for algorithm development and evaluation. The method systematically integrates or-bit propagation, high-precision astrometric corrections, imaging visibility constraints, and multi-source noise modeling. A unified Point Spread Function convolution streak model is established to consistently represent the motion blur of both stars and space objects during exposure. Additionally, simplified parametric stray light background models covering the Sun, Moon, and Earth airglow are constructed. Quantitative comparison with real image data from the Kaiyun-1 satellite demonstrates good agreement in star positions, streak morphology, and centroid localization accuracy. Preliminary validation against real data demonstrates that the proposed simulation framework can provide effective image data for testing and performance assessment of space-based situational awareness algorithms.
Sun et al. (2026) studied this question.