Systematic TLE data improvement by neural network for most cataloged resident space objects

Abstract

Two-line element sets (TLE) released by the 18th Space Control Squadron are the most complete source of information for space situational awareness in the public domain. They are used in a wide range of contexts that include navigation of nanosatellites and ground communication. However, the TLE data are known for having significant bias and errors. Predicting the bias of TLE can be a way to correct them automatically and hence optimize the systems that rely on them at low cost, for instance for reentry calculations and mission analysis. This paper shows the performance of a neural network trained on more than 4500 inactive resident space objects (RSO) on 10 months of orbital data, on all types of orbits. It is shown that the state vector errors are corrected by at least 40% for 70% of the TLE and that the residual error distributions are well described by a Student’s t-distribution for which covariance elements are defined consistently. The performance of the trained neural network are shown to be similar for multiple active satellites.