A Space Object Optical Scattering Characteristics Analysis Model Based on Augmented Implicit Neural Representation

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object's surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S-net, R-net, and F-net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F-net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 mV, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services.