Progress of light curve inversion technology for resident space object characteristics

Abstract

The characteristics of space objects such as shape, size, attitude, angular velocity, reflectivity and material play a significant role in certain application fields like space object identification, track prediction and collision avoidance. The ground space optical observation system as one of the main measurement methods to observe space objects is difficult to form high-solution images for small objects or objects located in LEO and MEO, which can only appear several light spots with limited pixels due to the impacts of atmospheric conditions, observing distance and resolution ratio. Through the light curves of space objects obtained from the ground-based optical observation system, the characteristics of objects can be inversed as it is closely related to the brightness of these objects. The technology based on the characteristics of ground-based light curves and objective modeling are two main ways of light curve inversion, through the analysis of inversion technology in recent 10 years. Overall, the principle of the inversion technology based on light curve features is ordinary with easy and fast calculations. Firstly, based on the periodic features of the light curves, the rotation period of a spinning space object can be accurately identified, so this method is widely used in the inversion of the characteristics of spinning space objects. Secondly, based on the general features of light curves, the operating state of a three axis stabilized space object can be identified visually. When the three axis stabilized space object was failure, the light curve often presents a periodic trend. Otherwise the light curve often has obvious periodic variation when the space object was in a normal operation state. In addition, the bus type of space object can be inversed because of the general features of light curve, thus we can immediately classified the space object. The upper and lower bounds of the angular velocity of the three axis stabilized space object can be estimated roughly according to the extremum features of light curves, in this way the attitude change of object can also be analyzed. The technology based on objective modeling improved the accuracy of inversion to a certain extent by establishing complex models. The principle of the light curve inversion methods for space objects relied on modeling the objects as a closed set of facets which is relatively simple. This method can determine characteristics like shapes and orientation of satellites, rocket bodies, and space debris as well, however, it is more suitable for convex objects’ characteristics inversion. The two-facet model simplified the space objects as the combination of two facets, one facet represents the solar panel and the other represents the body, but this simplification process reduced the inversion accuracy. The machine learning inversion method will obtain more accurate results, but it require enough historical light curves for modeling objects. If there have inadequate priori datas of a space object, this method often performs large error compared with the true value. The nonlinear filtering method can be used to accurately determine the size, shape, position, velocity, attitude, angular rates, surface parameter and other characteristics of the space object from sparse light curve data, and it can also detect the change of these characteristics. Therefore, this method has more superiority than other inversion techniques. This paper has summarized researching development of the inversion technology in recent 10 years. The purpose, advantages and disadvantages of each technology are analyzed in detail and the future research direction in this field is also proposed.