Short-Term TLE Uncertainty Estimation Using an Artificial Neural Network Model

A growing number of space activities have created an orbital debris environment that poses increasing impact risks to existing space systems and human space flight. Accurate knowledge of orbit propagation errors of space debris is essential for many types of analyses, such as space surveillance network tasking, conjunction analysis etc. Unfortunately, for two-line elements (TLEs) this is not available. In this paper, a new short-term TLE uncertainty estimation method based on an artificial neural network model is proposed. Object properties, orbit type, space environment and prediction time-span are considered as the input of the network, the propagation errors in the direction of downrange, normal and conormald are as the output of the network. In order to assure the chosen orbit for training is not for an object using station keeping, only debris and R/B are used. The network’s efficiency is demonstrated with some objects with high ephemeris data. Overall, the method proves accurate, computationally fast, and robust, and is applicable to any object in the satellite catalogue, especially for those newly launched objects.