Abstract
In this work, we have proposed a scattering spectra-based method for inverting the surface materials and material proportions of space objects (SOs) from long distances. The results of this work shall improve efforts to characterize and predict the orbits of space debris. We first constructed a physical model for SO characterization based on scattering spectra and then provided a least-squares solution with minimum-norm (LSMN) algorithm for inverting the surface materials and material proportions of an SO. The optical reflectance of complex material surfaces was characterized using a bidirectional reflectance distribution function (BRDF)-based multimodal fusion model that uses the characteristics of the light source, the reflectance of the target’s surface materials, and structures, and the angle of incidence and reflection. The area of each material in the BRDF was then treated as the to-be-inverted parameter. The proposed method was then experimentally validated using four sets of materials. The materials and proportions of equiproportional and non-equiproportional combinations of materials were inverted by the proposed method, and the average inversion error was less than 10%. According to the relationship curve be-tween experimental data error and inversion error, and between theoretical error and inversion error, it can be concluded that the accuracy of inversion error has a linear relationship with the measurement data error. In summary, we have provided a new technical approach for the inversion and characterization of SO materials and material proportions from long distances.
Highlights
Due to the rapid development of the aerospace industry, the number of space objects entering Earth’s orbit is increasing, as is the amount of space
We have proposed a scattering spectra-based method for inverting the surface materials and material proportions of space objects (SOs) from long distances
We first constructed a physical model for SO characterization based on scattering spectra and provided a least-squares solution with minimum-norm (LSMN) algorithm for inverting the surface materials and material proportions of an SO
Summary
Due to the rapid development of the aerospace industry, the number of space objects entering Earth’s orbit is increasing, as is the amount of space. If some piece of space debris survives re-entry and hits a densely populated area, the consequences could be utterly catastrophic [1] [2] [3] Due to these dangers, it is necessary to identify and classify all space debris, whose class and type can be inferred from their materials and material proportions. As SOs are located hundreds or thousands of kilometers away in low, medium, or geosynchronous orbits, it is usually only possible to obtain information from a single pixel, like position and luminosity, but not material, size, or shape. These limitations have made it extremely challenging to identify the attributes of an SO. The findings of this study provide a novel approach for SO characterization that will have significant implications for the characterization and identification of space debris
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