Elaborate imaging simulation and validation of the space target based on surface fractal features

In order to meet the urgent needs of target image data in the testing of close-range space target situational awareness algorithm, this study develops a method for generating high-fidelity space target simulations by integrating refined wrinkled surface modeling with physics-based optical characterization. The proposed method combines fractal theory and multi-scale superposition principles to model satellite surface topography, integrating both macro-scale bulge-stripe patterns and micro-scale wrinkled textures. By analyzing autocorrelation characteristics of wrinkled structures, a mathematical model was established and subsequently optimized using material-specific particle swarm algorithms. Experimental validation demonstrated that the optimized model achieved goodness-of-fit metrics of 84.83% and 86.16% for two typical thermal control materials, confirming its material-adaptive capability. The optical characterization framework employed a five-parameter bidirectional reflectance distribution function (BRDF) model to describe surface scattering properties. Through the ray-tracing method, sequential images of space targets were computationally generated. A ground experimental platform is established to capture real image data of the reduced target model, and the imaging conditions and simulation steps are designed to meet the actual observation scene. Comparative analysis between simulated and ground-measured images revealed a structural similarity index exceeding 85%, indicating satisfactory agreement in both geometric features and radiometric characteristics. This method accurately replicates on-orbit targets' optical signatures by combining multi-scale surface modeling with physics-based rendering, generating realistic data for validating space situational awareness algorithms in high-fidelity optical reconstruction scenarios.